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DOE OSTI2018

CFD-DEM solution verification: Fixed-bed studies

Fixed (frozen) particle configurations are used to study CFD-DEM convergence behavior in the limit of infinite grid resolution. A filtering scheme based on solving a diffusion equation, i.e., a Gaussian kernel, is used to interpolate particle information to the continuum grid, free of cell size limitations. A regression method is used for Richardson extrapolation of the discrete solutions to the grid-free solution. The regression method is shown to predict accurate extrapolation curves in the presence of the observed oscillatory convergence behavior. The extrapolation curve is used to complete the solution verification process by quantifying the numerical uncertainty present in the calculations. Several numerical and model parameters are studied including the spatial discretization method, the kernel width and the mean drag law. Here, a manageable level of discretization error is found with relative discretization errors generally <4%.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. Here, to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

42 ENGINEERING↗

DOE OSTI2019

Correlating results from high resolution EBSD with TEM- and ECCI-based dislocation microscopy: Approaching single dislocation sensitivity via noise reduction

High resolution electron backscatter diffraction (HREBSD), an SEM-based diffraction technique, may be used to measure the lattice distortion of a crystalline material and to infer the geometrically necessary dislocation content. Uncertainty in the image correlation process used to compare diffraction patterns leads to an uneven distribution of measurement noise in terms of the lattice distortion, which results in erroneous identification of dislocation type and density. This work presents a method of reducing noise in HREBSD dislocation measurements by removing the effect of the most problematic components of the measured distortion. The method is then validated by comparing with TEM analysis of dislocation pile-ups near a twin boundary in austenitic stainless steel and with ECCI analysis near a nano-indentation on a tantalum oligocrystal. Finally, the HREBSD dislocation microscopy technique is able to resolve individual dislocations visible in TEM and ECCI and correctly identify their Burgers vectors.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The Role of Subsurface Flow on Evapotranspiration: A Global Sensitivity Analysis

Abstract Water resources are impacted by water‐energy balance fluxes at the land surface, most notably evapotranspiration ( ET ), the largest component of surface energy balance. While integrated hydrologic models show promise in quantifying the nonlinear dynamics at this interface, the model results are plagued by parametric uncertainties. Given the high computational demand of running multiple parameter spaces of these models, little is known about how these uncertainties propagate into land surface processes. In this work, we perform a global sensitivity analysis by computing Sobol and AMAE indices using a surrogate model constructed with a polynomial chaos expansion to assess the impacts of the subsurface physical properties on ET . We do so by modeling a semisynthetic test case. Our results show that the effects of vertical hydraulic conductivity, porosity, and the water retention curve parameter Van Genuchten α mainly control ET . However, we note that while evaporation ( E ) shows behavior similar to the ET with high sensitivity to the parameters controlling the flow in the unsaturated zone, transpiration ( T ) is very sensitive to the saturated zone parameters and groundwater flow, especially during periods without rain. Our results also show that heterogeneities in land cover impact the flow in the saturated zone. Furthermore, our work demonstrates that reduced‐order models can be developed to make integrated models more accessible for rigorous sensitivity analyses and calibration purposes.

Maina, Fadji Zaouna↗

DOE OSTI2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗

DOE OSTI2022

Lossy compression of statistical data using quantum annealer

Abstract We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3–3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Constraining Nucleosythesis in Neutrino-Driven Winds Using the Impact of ({alpha}, xn) Reaction Rates

The lighter heavy elements of the first r-process peak, between strontium and silver, can be synthesized in the moderately neutron-rich neutrino-driven ejecta of either core-collapse supernovae or neutron star mergers via the weak r-process. This nucleosynthesis scenario exhibits uncertainties from the absence of experimental data from (alpha, xn) reactions on neutron-rich nuclei, which are currently based on statistical model estimates. We have performed a new impact study to identify the most important (alpha, xn) reactions that can affect the production of the lighter heavy elements under different astrophysical conditions using new, constrained (alpha, xn) reaction rates based on the Atomki-V2 alpha OMP. Our results show how when reducing the nuclear physics uncertainties, we can use abundance ratios to constrain the astrophysical conditions/environment. This can be achieved in the near future, when the key (alpha, xn) reaction rates will be measured experimentally in radioactive beam facilities.

Psaltis, Athanasios↗

DOE OSTI2014

Challenges in nucleosynthesis of trans-iron elements

Nucleosynthesis beyond Fe poses additional challenges not encountered when studying astrophysical processes involving light nuclei. Astrophysical sites and conditions are not well known for some of the processes involved. On the nuclear physics side, different approaches are required, both in theory and experiment. The main differences and most important considerations are presented for a selection of nucleosynthesis processes and reactions, specifically the s-, r-, γ-, and νp-processes. Among the discussed issues are uncertainties in sites and production conditions, the difference between laboratory and stellar rates, reaction mechanisms, important transitions, thermal population of excited states, and uncertainty estimates for stellar rates. The utility and limitations of indirect experimental approaches are also addressed. The presentation should not be viewed as confining the discussed problems to the specific processes. The intention is to generally introduce the concepts and possible pitfalls along with some examples. Similar problems may apply to further astrophysical processes involving nuclei from the Fe region upward and/or at high plasma temperatures. The framework and strategies presented here are intended to aid the conception of future experimental and theoretical approaches.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Probabilistic flux limiters

The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical discretization in under-resolved simulations is to use a flux limiter. A wide range of flux limiters have been studied in the literature, with recent interest in their optimization via machine learning methods trained on high-resolution datasets. The common use of flux limiters in numerical codes as plug-and-play blackbox components makes them key targets for design improvement. Even for deterministic dynamical models, numerical uncertainty is introduced via coarse-graining required by insufficient computational power to solve all scales of motion. Conventional flux limiters are deterministic and lack the capacity to address uncertainties, both aleatoric (inherent randomness) and epistemic (modeling uncertainty due to limited knowledge), which arise in coarse-grained numerical simulations. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model and uncertainty in model parameters. Unlike traditional single-function flux limiters, these new probabilistic flux limiters incorporate multiple flux limiting functions, each applied with a learned probability drawn from high-resolution data to mitigate the effects of uncertainty in numerical simulations. This approach departs from traditional single-function limiters by explicitly modeling and incorporating uncertainty into the shock capturing process. Using the example of Burgers' equation as a testbed, we show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles. In particular, we show that our probabilistic flux limiter outperforms standard limiters and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Hunting a wandering supermassive black hole in the M31 halo hermitage

In the hierarchical structure formation scenario, galaxies enlarge through multiple merging events with less massive galaxies. In addition, the Magorrian relation indicates that almost all galaxies are occupied by a central supermassive black hole (SMBH) of mass 10{sup –3} times the mass of its spheroidal component. Consequently, SMBHs are expected to wander in the halos of their host galaxies following a galaxy collision, although evidence of this activity is currently lacking. We investigate a current plausible location of an SMBH wandering in the halo of the Andromeda galaxy (M31). According to theoretical studies of N-body simulations, some of the many substructures in the M31 halo are remnants of a minor merger occurring about 1 Gyr ago. First, to evaluate the possible parameter space of the infalling orbit of the progenitor, we perform numerous parameter studies using a graphics processing unit cluster. To reduce uncertainties in the predicted position of the expected SMBH, we then calculate the time evolution of the SMBH in the progenitor dwarf galaxy from N-body simulations using the plausible parameter sets. Our results show that the SMBH lies within the halo (∼20-50 kpc from the M31 center), closer to the Milky Way than the M31 disk. Furthermore, the predicted current positions of the SMBH were restricted to an observational field of 0.°6 × 0.°7 in the northeast region of the M31 halo. We also discuss the origin of the infalling orbit of the satellite galaxy and its relationships with the recently discovered vast thin disk plane of satellite galaxies around M31.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Aemulus ν: precision halo mass functions in wνCDM cosmologies

Precise and accurate predictions of the halo mass function for cluster mass scales in wνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys. Here, we present a halo mass function emulator for cluster mass scales (≳ 1013 M ⊙/h) up to redshift z = 2 with comprehensive support for the parameter space of wνCDM cosmologies allowed by current data. Based on the Aemulus ν suite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models. This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies. We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies. Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.

cluster counts↗

DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗

DOE OSTI2016

Investigations on RELAP5-3D to RELAP5-3D coupling methodology by PVMEXEC

In the framework of a BEPU (Best Estimate Plus Uncertainty) approach within the licensing process of a nuclear power plant, the need to extend the resources of nuclear system thermal-hydraulics codes, such as RELAP5-3D, arises to allow more detailed simulations of the complex 3D reality of Nuclear Power Plants (NPPs), either under normal steady-state or during various accident scenarios. Currently, it is not possible to achieve the same degree of detail for a whole nuclear system when it is simulated with RELAP5-3D and this is due to the inherent limitations in the number of components and volumes to be used for the analysis. For this reason, it is of extreme interest the use of tools for codes coupling that enable the use of different codes for the simulation of different portions of a system in a unified analysis. In this paper the attention will be focused on the decomposition of the thermal-hydraulic domain of a system into subsystems to be simulated by different instances of the same code (e.g. RELAP5-3D) coupled together by means of PVMEXEC program and parallel virtual machine (PVM) technology. Explicit and semi-implicit solution algorithms were used for the analyses. Among the analyzed cases, the following will be discussed in detail with the aim to provide additional guidelines for the use of the PVMEXEC tool: (i) the Edward's pipe blowdown test, (ii) a simplified countercurrent heat exchanger, (iii) different hydraulics and heat structure coupling schemes for a shell-tube heat exchanger and (iv) a three-task coupled model of an adaptation of the Christensen subcooled boiling experiment. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Measurement of top quark-antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

The measurement of a cross section for the production of top quark–antiquark pairs (tt¯) in association with a vector boson V (W or Z) in proton-proton collisions at √s=8 TeV is presented. The results are based on a dataset corresponding to an integrated luminosity of 19.5 fb -1 recorded with the CMS detector at the LHC. The measurement is performed in three leptonic (e and μ) channels: a same-sign dilepton analysis targeting tt¯W events, and trilepton and four-lepton analyses designed for tt¯Z events. In the same-sign dilepton channel, the tt¯W cross section is measured as σ tt¯W =170 +90 -80 (stat)±70(syst)fb, corresponding to a significance of 1.6 standard deviations over the background-only hypothesis. Combining the trilepton and four-lepton channels, a direct measurement of the tt¯Z cross section, σ tt¯Z =200 +80 -70 (stat) +40 -30 (syst)fb -1 , is obtained with a significance of 3.1 standard deviations. Finally, the measured cross sections are compatible with standard model predictions within their experimental uncertainties. The inclusive tt¯V process is observed with a significance of 3.7 standard deviations from the combination of all three leptonic channels.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

CalWater Field Studies Designed to Quantify the Roles of Atmospheric Rivers and Aerosols in Modulating U.S. West Coast Precipitation in a Changing Climate

The variability of precipitation and water supply along the U.S. West Coast creates major challenges to the region’s economy and environment, as evidenced by the recent California drought. This variability is strongly influenced by atmospheric rivers (AR), which deliver much of the precipitation along the U.S. West Coast and can cause flooding, and by aerosols (from local sources and transported from remote continents and oceans) that modulate clouds and precipitation. A better understanding of these processes is needed to reduce uncertainties in weather predictions and climate projections of droughts and floods, both now and under changing climate conditions.To address these gaps a group of meteorologists, hydrologists, climate scientists, atmospheric chemists, and oceanographers have created an interdisciplinary research effort, with support from multiple agencies. From 2009-2011 a series of field campaigns (CalWater 1) collected atmospheric chemistry, cloud microphysics and meteorological measurements in California and associated modeling and diagnostic studies were carried out. Based on remaining gaps, a vision was developed to extend these studies offshore over the Eastern North Pacific and to enhance land based measurements from 2014-2018 (CalWater 2). The data set and selected results from CalWater 1 are summarized here. The goals of CalWater-2, and measurements to date, are then described. Through this work, CalWater is producing new findings and exploring new technologies to evaluate and improve global climate models and their regional performance and to develop tools supporting water and hydropower management. These advances also have potential to enhance hazard mitigation by improving near-term weather prediction and subseasonal and seasonal outlooks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

International land Model Benchmarking (ILAMB) Package v001.00

As a contribution to International Land Model Benchmarking (ILAMB) Project, we are providing new analysis approaches, benchmarking tools, and science leadership. The goal of ILAMB is to assess and improve the performance of land models through international cooperation and to inform the design of new measurement campaigns and field studies to reduce uncertainties associated with key biogeochemical processes and feedbacks. ILAMB is expected to be a primary analysis tool for CMIP6 and future model-data intercomparison experiments. This team has developed initial prototype benchmarking systems for ILAMB, which will be improved and extended to include ocean model metrics and diagnostics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

International land Model Benchmarking (ILAMB) Package v002.00

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Seismic and Infrasound Location

This presentation includes slides on Signal Propagation Through the Earth/Atmosphere Varies at Different Scales; 3D Seismic Models: RSTT; Ray Coverage (Pn); Source-Specific Station Corrections (SSSCs); RSTT Conclusions; SALSA3D (SAndia LoS Alamos) Global 3D Earth Model for Travel Time; Comparison of IDC SSSCs to RSTT Predictions; SALSA3D; Validation and Model Comparison; DSS Lines in the Siberian Platform; DSS Line CRA-4 Comparison; Travel Time Δak135; Travel Time Prediction Uncertainty; SALSA3D Conclusions; Infrasound Data Processing: An example event; Infrasound Data Processing: An example event; Infrasound Location; How does BISL work?; BISL: Application to the 2013 DPRK Test; and BISL: Ongoing Research.

58 GEOSCIENCES↗