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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

OpenMP Target Task: Tasking and Target Offloading on Heterogeneous Systems

This work evaluated the use of OpenMP tasking with target GPU offloading as a potential solution for programming productivity and performance on heterogeneous systems. Also, it is proposed a new OpenMP specification to make the implementation of heterogeneous codes simpler by using OpenMP target task, which integrates both OpenMP tasking and target GPU offloading in a single OpenMP pragma. As a test case, the authors used one of the most popular and widely used Basic Linear Algebra Subprogram Level-3 routines: triangular solver (TRSM). To benefit from the heterogeneity of the current high-performance computing systems, the authors propose a different parallelization of the algorithm by using a nonuniform decomposition of the problem. This work used target GPU offloading inside OpenMP tasks to address the heterogeneity found in the hardware. This new approach can outperform the state-of-the-art algorithms, which use a uniform decomposition of the data, on both the CPU-only and hybrid CPU-GPU systems, reaching speedups of up to one order of magnitude. The performance that this approach achieves is faster than the IBM ESSL math library on CPU and competitive relative to a highly optimized heterogeneous CUDA version. One node of Oak Ridge National Laboratory’s supercomputer, Summit, was used for performance analysis.

Valero Lara, Pedro↗
DOE OSTI2023

Exploring the parameter space of MagLIF implosions using similarity scaling. III. Rise-time scaling

Magnetized liner inertial fusion (MagLIF) is a z-pinch magneto-inertial-fusion concept studied at the Z Pulsed Power Facility of Sandia National Laboratories. Two important metrics characterizing current delivery to a z-pinch load are the peak current and the current-rise time, which is roughly the time interval to reach the peak current. It is known that, when driving a z-pinch load with a longer current-rise time, the performance of the z-pinch decreases. However, a theory to understand and quantify this effect is still lacking. Here, in this paper, we utilize a framework based on similarity scaling to analytically investigate the variations in the performance of MagLIF loads when varying the current-rise time, or equivalently, the implosion timescale. To maintain similarity between the implosions, we provide scaling prescriptions of experimental input parameters defining a MagLIF load and derive the expected scaling laws for stagnation conditions and for various performance metrics. We compare predictions of the theory to 2D numerical simulations using the radiation, magneto-hydrodynamic code hydra. For several metrics, we find acceptable agreement between the theory and simulations. Our results show that the voltage φload near the MagLIF load follows a weak scaling law φload ∝$t_{φ}^{-0.12}$ with respect to the characteristic timescale t φ of the voltage source, instead of the ideal φload ∝$t_{φ}^{-1}$ scaling. This occurs because the imploding height of the MagLIF load must increase to preserve end losses. As a consequence of the longer imploding liners, the required total laser preheat energy and delivered electric energy increase. Overall, this study helps understand the trade-offs of the MagLIF design space when considering future pulsed-power generators with shorter and longer current-rise times.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

LINE EMISSION FROM RADIATION-PRESSURIZED H II REGIONS. I. INTERNAL STRUCTURE AND LINE RATIOS

The emission line ratios [O III] {lambda}5007/H{beta} and [N II] {lambda}6584/H{alpha} have been adopted as an empirical way to distinguish between the fundamentally different mechanisms of ionization in emission-line galaxies. However, detailed interpretation of these diagnostics requires calculations of the internal structure of the emitting H II regions, and these calculations depend on the assumptions one makes about the relative importance of radiation pressure and stellar winds. In this paper, we construct a grid of quasi-static H II region models to explore how choices about these parameters alter H II regions' emission line ratios. We find that when radiation pressure is included in our models, H II regions reach a saturation point beyond which further increase in the luminosity of the driving stars does not produce any further increase in effective ionization parameter, and thus does not yield any further alteration in an H II region's line ratio. We also show that if stellar winds are assumed to be strong, the maximum possible ionization parameter is quite low. As a result of this effect, it is inconsistent to simultaneously assume that H II regions are wind-blown bubbles and that they have high ionization parameters; some popular H II region models suffer from this inconsistency. Our work in this paper provides a foundation for a companion paper in which we embed the model grids we compute here within a population synthesis code that enables us to compute the integrated line emission from galactic populations of H II regions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Few-shot leaf segmentation

In this work, we use few-shot learning to segment the body and vein architecture of Populus trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes.

Lagergren, John [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2023

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗
DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Extension of the ADAPT Framework for Multiple Simulators

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One limitation of the DDET driver codes developed to date (namely ADAPT, ADS, and MCDET ) is that the tree is driven by a single simulator. The goal of this work is a general multi-simulator driver. This limits the parameter space that may be explored in a single DDET to that which may be covered by a single simulator. In this work, the Analysis of Dynamic Accident Progression Trees (ADAPT) code is extended to support multiple arbitrary simulators. This capability will increase the depth and breadth of phenomena that may be analyzed in a single DDET. For example, a large-scale system code may pass the analysis to a more detailed code if certain conditions are met, in order to capture relevant phenomena that would be lost if only the system code were to be used. In addition, the time required to cover a given uncertainty space may be reduced by dynamically identifying and running only the situations that are appropriate given the plant conditions in each simulator. The use of multiple simulators is expected to further reduce subjectivity in PRA by enabling high fidelity modeling of reality. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Dynamic Importance Measures in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until user-defined end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One current limitation of DDETs is the assessment of results, which is not as well developed as in traditional PRA. A concept that is used in PRA to assess the significance of a basic event is the concept of importance measures (IMs). In traditional PRA, IMs only consider the likelihood of occurrence of non-occurrence of a basic event. The application of importance measures to DDETs must take into consideration not only the occurrence and non-occurrence of an event, but also uncertain timing or severity. This work describes three general dynamic importance measures (DIMs) as implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The measures account for the change in progression of the DDET resulting from the different values of each uncertain variable under investigation. The DIMs are applied to a sample DDET to demonstrate the type of insights that may be gained from each one. These measures will facilitate comparison of the impact of dynamic branching events on a consequence of interest, which also allow DDETs to be used in prioritizing plant investments. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Minibeam radiation therapy for the management of osteosarcomas: A Monte Carlo study

Purpose: Minibeam radiation therapy (MBRT) exploits the well-established tissue-sparing effect provided by the combination of submillimetric field sizes and a spatial fractionation of the dose. The aim of this work is to evaluate the feasibility and potential therapeutic gain of MBRT, in comparison with conventional radiotherapy, for osteosarcoma treatments. Methods: Monte Carlo simulations (PENELOPE/PENEASY code) were used as a method to study the dose distributions resulting from MBRT irradiations of a rat femur and a realistic human femur phantoms. As a figure of merit, peak and valley doses and peak-to-valley dose ratios (PVDR) were assessed. Conversion of absorbed dose to normalized total dose (NTD) was performed in the human case. Several field sizes and irradiation geometries were evaluated. Results: It is feasible to deliver a uniform dose distribution in the target while the healthy tissue benefits from a spatial fractionation of the dose. Very high PVDR values (⩾20) were achieved in the entrance beam path in the rat case. PVDR values ranged from 2 to 9 in the human phantom. NTD{sub 2.0} of 87 Gy might be reached in the tumor in the human femur while the healthy tissues might receive valley NTD{sub 2.0} lower than 20 Gy. The doses in the tumor and healthy tissues might be significantly higher and lower than the ones commonly delivered used in conventional radiotherapy. Conclusions: The obtained dose distributions indicate that a gain in normal tissue sparing might be expected. This would allow the use of higher (and potentially curative) doses in the tumor. Biological experiments are warranted.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

HPC4Mfg with Samsung: Making semiconductor devices cool through HPC ab initio simulations

For decades, the semiconductor technology has followed the Moore’s law, making newer devices more powerful and energy efficient. Recently, however, it has reached a point where the performance and the energy efficiency of the device do not improve with the shrinking device size. One of the fundamental reasons of this deviation from the past trend is the interconnect resistance, which becomes larger with the shrinking size. The devices size is so small that the quantum mechanical effects can no longer be ignored and the traditional continuum simulation tools such as TCAD become inadequate. In this project, Samsung Semiconductor Inc. and Lawrence Berkeley National Laboratory has collaborated to perform first of kind device-scale ab initio simulations to optimize materials and interconnect morphology to minimize interconnect resistance. We have tested the use of LS3DF method and the PEtot_trans approach on top of the folded spectrum method (FSM) Escan code to calculate the scattering state, and to study various effects influence the interconnect conductivity. We found that, the LS3DF can be used to calculate such metallic system. On the other hand, the use of Escan code to solve the linear equation is not practical due to the slow convergence. We have implemented a Chebyshev filter technique to calculate a few hundred eigen states near the scattering state energy E, then use these eigen states as preconditioner to solve the linear equation. We have used this approach to study the different factors which affect the interconnect conductivity, including the shape, the point defect, the temperature, and the grain boundary.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Variant profiling of evolving prokaryotic populations

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap . In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Magnetohydrodynamic simulations preliminarily predict the habitability and radio emission of TRAPPIST-1e

TRAPPIST-1e, an Earth-sized exoplanet in the habitable zone of the nearby M dwarf TRAPPIST-1, may experience magnetospheric responses that vary with stellar space weather, which could potentially influence both its habitability and radio emissions. Our objective is to investigate how different Earth-like magnetospheric configurations of TRAPPIST-1e – specifically variations in dipolar magnetic field strength and axial tilt – respond to diverse stellar space weather conditions, including events analogous to coronal mass ejections (CMEs), and to assess their implications for potential habitability and expected radio emissions. We conducted 3D magnetohydrodynamic simulations of the TRAPPIST-1e system using the PLUTO code in spherical coordinates. The planetary magnetic field was modelled as dipolar, with equatorial strengths from Earth-like to several times stronger. The dipole axis spans a representative range of axial tilts. We investigate four stellar wind environments, from sub-Alfvénic flow to CME-like disturbances. Planetary shielding was quantified based on the magnetopause standoff distance, and radio powers were estimated via empirical scaling laws. Our simulations show that both shielding and radio power depend strongly on the magnetic configuration. Stronger fields increase protection, while larger tilts reduce it. Radio power increases with both field strength and tilt across all wind regimes. An Earth-like magnetic field can provide effective shielding even under intense CMEs, whereas high tilts require stronger fields. Predicted radio powers reach ~10 20 erg s −1 during CMEs, making bursts from close-in, magnetised planets more detectable. However, for TRAPPIST-1e, the maximum cyclotron frequency lies below the Earth’s ionospheric cutoff (~10 MHz), making ground-based detection currently infeasible.

Sun: magnetic fields↗
DOE OSTI2013

Industrial and natural sources of gaseous elemental mercury in the Almadén district (Spain): An updated report on this issue after the ceasing of mining and metallurgical activities in 2003 and major land reclamation works

Two events during the last decade had major environmental repercussions in Almadén town (Spain). First it was the ceasing of activities in the mercury mine and metallurgical facilities in 2003, and then the finalization of the restoration works on the main waste dump in 2008. The combination of both events brought about a dramatic drop in the emissions of gaseous elemental mercury (GEM) to the atmosphere. Although no one would now call the Almadén area as ‘mercury-free’, the GEM levels have fallen beneath international reference safety levels for the first time in centuries. This has been a major breakthrough because in less than one decade the site went from GEM levels in the order of “tens of thousands” to mere “tens” nanogram per cubic meter. Although these figures are per se a remarkable achievement, they do not mark the end of the environmental concerns in the Almadén district. Two other sites remain as potential environmental hazards. (1) The Las Cuevas mercury storage complex, a partially restored ex-mining site where liquid mercury is being stored. The MERSADE Project (LIFE—European Union) has tested the Las Cuevas complex as a potential site for the installation of a future European prototype safe deposit of surplus mercury from industrial activities. Despite restoration works carried out in 2004, the Las Cuevas complex can still be regarded as hotspot of mercury contamination, with high concentrations above 800 μg g{sup −1} Hg{sub soil} and 300 ng m{sup −3} Hg{sub gas}. However, as predicted by air contamination modeling using the ISC-AERMOD software, GEM concentrations fade away in a short distance following the formation of a NW–SE oriented narrow plume extending for a few hundred meters from the complex perimeter. (2) Far more dangerous from the human health perspective is the Almadenejos area, hosting the small Almadenejos village, the so-called Cerco de Almadenejos (CDA; an old metallurgical precinct), and the mines of La Nueva Concepción, La Vieja Concepción and El Entredicho. The CDA is an old metallurgical site that operated between 1794 and 1861, leaving behind a legacy of extremely contaminated soils (mean concentration=4220 μg g{sup −1} Hg) and GEM emissions that in summer can reach levels up to 4,000–5,000 ng m{sup −3}. Thus the CDA remains the sole ‘urban’ site in the district surpassing GEM international reference safety levels. In order to prevent these emissions, the CDA requires immediate action regarding restoration works. These could involve the full removal of soils or their permanent capping to create an impermeable barrier.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

THE IMPACT OF TYPE Ia SUPERNOVA EXPLOSIONS ON HELIUM COMPANIONS IN THE CHANDRASEKHAR-MASS EXPLOSION SCENARIO

In the version of the single-degenerate scenario of Type Ia supernovae (SNe Ia) studied here, a carbon-oxygen white dwarf explodes close to the Chandrasekhar limit after accreting material from a non-degenerate helium (He) companion star. In the present study, we employ the STELLAR GADGET code to perform three-dimensional hydrodynamical simulations of the interaction of the SN Ia ejecta with the He companion star taking into account its orbital motion and spin. It is found that only 2%-5% of the initial companion mass is stripped off from the outer layers of He companion stars due to the supernova (SN) impact. The dependence of the unbound mass (or the kick velocity) on the orbital separation can be fitted to a good approximation by a power law for a given companion model. After the SN impact, the outer layers of a He donor star are significantly enriched with heavy elements from the low-expansion-velocity tail of SN Ia ejecta. The total mass of accumulated SN-ejecta material on the companion surface reaches about {approx}> 10{sup -3} M{sub Sun} for different companion models. This enrichment with heavy elements provides a potential way to observationally identify the surviving companion star in SN remnants. Finally, by artificially adjusting the explosion energy of the W7 explosion model, we find that the total accumulation of SN ejecta on the companion surface is also dependent on the explosion energy with a power-law relation to a good approximation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Mass transport and turbulence in gravitationally unstable disk galaxies. I. The case of pure self-gravity

The role of gravitational instability-driven turbulence in determining the structure and evolution of disk galaxies, and the extent to which gravity rather than feedback can explain galaxy properties, remains an open question. To address it, we present high-resolution adaptive mesh refinement simulations of Milky Way-like isolated disk galaxies, including realistic heating and cooling rates and a physically motivated prescription for star formation, but no form of star formation feedback. After an initial transient, our galaxies reach a state of fully nonlinear gravitational instability. In this state, gravity drives turbulence and radial inflow. Despite the lack of feedback, the gas in our galaxy models shows substantial turbulent velocity dispersions, indicating that gravitational instability alone may be able to power the velocity dispersions observed in nearby disk galaxies on 100 pc scales. Moreover, the rate of mass transport produced by this turbulence approaches ∼1 M{sub ⊙} yr{sup −1} for Milky Way-like conditions, sufficient to fully fuel star formation in the inner disks of galaxies. In a companion paper, we add feedback to our models, and use the comparison between the two cases to understand which galaxy properties depend sensitively on feedback and which can be understood as the product of gravity alone. All of the code, initial conditions, and simulation data for our model are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Toward a complete theory for predicting inclusive deuteron breakup away from stability

We present an account of the current status of the theoretical treatment of inclusive (d, p) reactions in the breakup-fusion formalism, pointing to some applications and making the connection with current experimental capabilities. Three independent implementations of the reaction formalism have been recently developed, making use of different numerical strategies. The codes also originally relied on two different but equivalent representations, namely the prior (Udagawa-Tamura, UT) and the post (Ichimura-Austern-Vincent, IAV) representations. The different implementations have been benchmarked for the first time, and then applied to the Ca isotopic chain. The neutron-Ca propagator is described in the Dispersive Optical Model (DOM) framework, and the interplay between elastic breakup (EB) and non-elastic breakup (NEB) is studied for three Ca isotopes at two different bombarding energies. The accuracy of the description of different reaction observables is assessed by comparing with experimental data of (d, p) on 40,48Ca. We discuss the predictions of the model for the extreme case of an isotope (60Ca) currently unavailable experimentally, though possibly available in future facilities (nominally within production reach at FRIB). We explore the use of (d, p) reactions as surrogates for (n,γ) processes, by using the formalism to describe the compound nucleus formation in a (d,pγ) reaction as a function of excitation energy, spin, and parity. The subsequent decay is then computed within a Hauser-Feshbach formalism. Comparisons between the (d,pγ) and (n,γ) induced gamma decay spectra are discussed to inform efforts to infer neutron captures from (d,pγ) reactions. Finally, we identify areas of opportunity for future developments, and discuss a possible path toward a predictive reaction theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗