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At least 613 records · Page 34

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DOE OSTI2016

Multi-fidelity machine learning models for accurate bandgap predictions of solids

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography

Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.

deep learning↗

DOE OSTI2024

Numerical eigen-spectrum slicing, accurate orthogonal eigen-basis, and mixed-precision eigenvalue refinement using OpenMP data-dependent tasks and accelerator offload

Performing a variety of numerical computations efficiently and, at the same time, in a portable fashion requires both an overarching design followed by a number of implementation strategies. All of these are exemplified below as we present transitioning the PLASMA numerical library from relying on dependence-driven large tasks to achieving utilization of fine grain tasking and offload to hardware accelerators while keeping its core dependence sets: OpenMP source code pragmas and runtime for most system-level functionality and basic low-level numerical kernels provided directly by hardware vendors or open source projects with vendor contributions. We also present new algorithmic methods and their efficient parallel implementations including fine grained tasking for eigen-spectrum slicing and offload for mixed-precision eigenvalue refinement. We provide performance, scaling, and numerical results showing sizable gains over the available solutions from either the open source and vendor-provided packages.

Luszczek, Piotr↗

DOE OSTI2011

Transport Test Problems for Hybrid Methods Development

This report presents 9 test problems to guide testing and development of hybrid calculations for the ADVANTG code at ORNL. These test cases can be used for comparing different types of radiation transport calculations, as well as for guiding the development of variance reduction methods. Cases are drawn primarily from existing or previous calculations with a preference for cases which include experimental data, or otherwise have results with a high level of confidence, are non-sensitive, and represent problem sets of interest to NA-22.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

Ventilation Control of Volatile Organic Compounds in New U.S. Homes: Results of a Controlled Field Study in Nine Residential Units

In order to optimize strategies to remove airborne contaminants in residences, it is necessary to determine how contaminant concentrations respond to changes in the air exchange rate. The impact of air exchange rate on the indoor concentrations of 39 target volatile organic compounds (VOCs) was assessed by measuring air exchange rates and VOC concentrations at three ventilation settings in nine residences. Active sampling methods were used for VOC concentration measurements, and passive perfluorocarbon tracer gas emitters with active sampling were used to determine the overall air exchange rate corresponding to the VOC measurements at each ventilation setting. The concentration levels and emission rates of the target VOCs varied by as much as two orders of magnitude across sites. Aldehyde and terpene compounds were typically the chemical classes with highest concentrations, followed by alkanes, aromatics, and siloxanes. For each home, VOC concentrations tended to decrease as the air exchange rate was increased, however, measurement uncertainty was significant. The indoor concentration was inversely proportional to air exchange rate for most compounds. For a subset of compounds including formaldehyde, however, the indoor concentration exhibited a non-linear dependence on the timescale for air exchange.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Ventilation Control of Volatile Organic Compounds in New U.S. Homes: Results of a Controlled Field Study in Nine Residential Units

42 ENGINEERING↗

DOE OSTI2024

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\rm{std}(\sigma_{\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings.

Nevin, Rebecca↗

DOE OSTI2018

Modeling underwater noise propagation from marine hydrokinetic power devices through a time-domain, velocity-pressure solution

Marine hydrokinetic (MHK) devices generate electricity from the motion of tidal and ocean currents, as well as ocean waves, to provide an additional source of renewable energy available to the United States. These devices are a source of anthropogenic noise in the marine ecosystem and must meet regulatory guidelines that mandate a maximum amount of noise that may be generated. In the absence of measured levels from in situ deployments, a model for predicting the propagation of sound from an array of MHK sources in a real environment is essential. A set of coupled, linearized velocity-pressure equations in the time-domain are derived and presented in this paper, which are an alternative solution to the Helmholtz and wave equation methods traditionally employed. Discretizing these equations on a three-dimensional (3D), finite-difference grid ultimately permits a finite number of complex sources and spatially varying sound speeds, bathymetry, and bed composition. The solution to this system of equations has been parallelized in an acoustic-wave propagation package developed at Sandia National Labs, called Paracousti. This work presents the broadband sound pressure levels from a single source in two-dimensional (2D) ideal and Pekeris wave-guides and in a 3D domain with a sloping boundary. Furthermore, the paper concludes with demonstration of Paracousti for an array of MHK sources in a simple wave-guide.

13 HYDRO ENERGY↗

DOE OSTI2014

Risk-Informed Safety Margin Characterization Case Study: Selection of Electrical Equipment to Be Subjected to Environmental Qualification

The Risk-Informed Safety Margin Characterization (RISMC) pathway of the DOE’s Light Water Reactor Sustainability (LWRS) program focuses on advancing the state of the art in safety analysis and risk assessment to support decision-making on nuclear power plant operation well beyond the originally designed lifetime of the plants (i.e., beyond 60 years). Among the issues being addressed in RISMC is the significance of SSC aging and how confident we are about our understanding of its impact on the margin between the loads SSCs are expected to see during normal operation and accident conditions, and the SSC capacities (their ability to resist those loads) as the SSCs age. In this paper, a summary is provided of a case study that examines SSC aging from an environmental qualification (EQ) perspective. The case study illustrates how the state of knowledge regarding SSC margin can be characterized given the overall integrated plant design, and was developed to demonstrate a method for deciding on which cables to focus, which cables are not so important from an environmental qualification margin standpoint, and what plant design features or operating characteristics determine the role that environmental qualification plays in establishing a safety case on which decisions regarding margin can be made. The selection of cables for which demonstration of margin with respect to aging and environmental challenges uses a technique known as Prevention Analysis. Prevention Analysis is a Boolean method for optimal selection of SSCs (that is, those combinations of SSCs both necessary and sufficient to meet a predetermined selection criterion) in a manner that allows demonstration that plant-level safety can be demonstrated by the collection of selected SSCs alone. Choosing the set of SSCs that is necessary and sufficient to satisfy the safety objectives, and demonstrating that the safety objectives can be met effectively, determines where resources are best allocated to assure SSC performance margin. The paper describes the resulting component types that were selected by Prevention Analysis and identifies the accident sequence characteristics that cause these component types to be important from an EQ and aging perspective (and, hence, worthwhile evaluating the extent of safety margin). In addition, component types not selected as needing significant margin from an EQ and aging perspective are discussed and an engineering rationale is developed justifying the lack of need to apply resources to demonstrating margin for these component types. This rationale is in terms of design features of the plant and operating characteristics that make these component types less important from an EQ and aging perspective. While the case study focuses on EQ and aging of equipment and cables located inside the containment of this PWR, the prevention analysis method is demonstrated to be an effective technique for identification of minimal collections of components that would be effective in managing safety for a variety of issues associated with aging and long-term operation of the fleet of plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Tunable and angle-insensitive plasmon resonances in graphene ribbon arrays with multispectral diffraction response

Plasmon resonances in graphene ribbon arrays are investigated numerically by means of the Finite Element Method. Numerical analysis shows that a series of multipolar resonances take place when graphene ribbon arrays are illuminated by a TM polarized electromagnetic wave. Moreover, these resonances are angle-independent, and can be tuned greatly by the width and the doping level of the graphene ribbons. Specifically, we demonstrate that for graphene arrays with several sets of graphene ribbons, which have different widths or doping levels, each of these multipolar resonances will be split into several ones. In addition, as plasmon resonances can confine electromagnetic field at the ribbon edges, graphene ribbons with different widths or doping levels offer intriguing application for electrically tunable spectral imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗

DOE OSTI2018

A modified homotopy method for solving the principal-agent bilevel programming problem

In this paper, the principal-agent bilevel programming problem with integral operator is considered, in which the upper-level object is the agent that maximizes its expected utility with respect to an agreed compensation contract. The constraints are the principal’s participation and the agent’s incentive compatibility. The latter is a lower-level optimization problem with respect to its private action. To solve an equivalent single-level nonconvex programming problem with integral operator, a modified homotopy method for solving the Karush–Kuhn–Tucker system is proposed. This method requires only an interior point and, not necessarily, a feasible initial approximation for the constraint shifting set. Global convergence is proven under some mild conditions. Numerical experiments were performed by our homotopy method as well as by fmincon in Matlab, LOQO and MINOS. The experiments showed that: designing a piecewise linear contract is much better than designing a piecewise constant contract and only needs to solve a much lower-dimensional optimization problem and hence needs much less computation time; the optimal value of the principal-agent model with designing piecewise linear contract tends to a limitation, while the discrete segments gradually increase; and finally, the proposed modified homotopy method is feasible and effective.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Calculations for energies, transition rates, and lifetimes in Al-like Kr XXIV

Using the second-order many-body perturbation theory (MBPT) method, a complete and accurate data set of excitation energies, lifetimes, wavelengths, and electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2), and magnetic quadrupole (M2) line strengths, transition rates, and oscillator strengths for the lowest 880 levels arising from the 3l{sup 3} (0≤l≤2), 3l{sup 2}4l{sup ′} (0≤l≤2, 0≤l{sup ′}≤3), 3s{sup 2}5l (0≤l≤4), 3p{sup 2}5l (0≤l≤1), and 3s3p5l (0≤l≤4) configurations in Al-like Kr XXIV is provided. Comparisons are made with available experimental and theoretical results. Our calculated energies are expected to be accurate enough to facilitate identifications of observed lines involving the n=4,5 levels. The complete data set is also useful for modeling and diagnosing fusion plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Could the description on polynuclear superhalogens by DFT be comparable with high-level ab initio results? A comparison between DFT and CCSD(T)

A systematic density functional theory study including 17 exchange-correlation functionals was performed on different types of superhalogens with high level coupled-cluster single double including perturbative triple excitations (CCSD(T)) results as the reference. The superhalogens selected here cover the ranges from mononuclear to polynuclear structures and from structures with halogen-atom ligands to those with non-halogen ligands, e.g., [MgX{sub 3}]{sup −}, [Mg{sub 2}X{sub 5}]{sup −}, and [Mg{sub 3}X{sub 7}]{sup −} (X = F, Cl, CN). It is clearly indicated that three double-hybrid functionals B2T-PLYP, B2GP-PLYP, B2K-PLYP as well as the range-separated hybrid functional ωB97X are capable of providing results which approach the accuracy at the CCSD(T) level. The basis set effect is usually moderate and, in most cases, it is enough to utilize the basis set of triple-ξ quality, e.g., Def2-TZVP. In addition, the results of the HF and MP2 method are also acceptable here, especially for polynuclear superhalogens where CCSD(T) is probably unpractical.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Initial Considerations for Large-Scale Carbon Removal in the United States

The Biden Administration has established a goal of removing 1 Gt carbon dioxide equivalents per year (CO 2e /yr) from the atmosphere and of achieving net zero greenhouse gas emissions (GHG) by 2050. Our team of leading academic and DOE national laboratory experts is conducting the first economy-wide technical evaluation of the options for achieving this carbon dioxide removal (CDR) goal. The net zero goal includes targets of 100% clean electricity by 2035 with 40% GHG emissions reductions by 2030, which helps set primary scenario boundaries for our analysis of the CDR supply curve. Our analysis will evaluate feasibility, performance, and costs on a county level for the entire USA (including AK and HI where possible), considering all removal methods that are currently well-enough developed for us to estimate the likely costs in 2050. We anticipate that more than 1 Gt CO 2e of removal will be available to the Nation. We will identify how much of each CO 2 removal approach is available in specific regions of the Nation and provide cumulative costs and volumes (a supply curve) by region for 2050. We expect to complete this detailed analysis by late 2023.

42 ENGINEERING↗

DOE OSTI2014

Heap/stack guard pages using a wakeup unit

A method and system for providing a memory access check on a processor including the steps of detecting accesses to a memory device including level-1 cache using a wakeup unit. The method includes invalidating level-1 cache ranges corresponding to a guard page, and configuring a plurality of wakeup address compare (WAC) registers to allow access to selected WAC registers. The method selects one of the plurality of WAC registers, and sets up a WAC register related to the guard page. The method configures the wakeup unit to interrupt on access of the selected WAC register. The method detects access of the memory device using the wakeup unit when a guard page is violated. The method generates an interrupt to the core using the wakeup unit, and determines the source of the interrupt. The method detects the activated WAC registers assigned to the violated guard page, and initiates a response.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Python and HPC for High Energy Physics Data Analyses

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗