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At least 595 records · Page 33

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DOE OSTI2017

Design Analysis of SNS Target StationBiological Shielding Monoligh with Proton Power Uprate

This report documents the analysis of the dose rate in the experiment area outside the Spallation Neutron Source (SNS) target station shielding monolith with proton beam energy of 1.3 GeV. The analysis implemented a coupled three dimensional (3D)/two dimensional (2D) approach that used both the Monte Carlo N-Particle Extended (MCNPX) 3D Monte Carlo code and the Discrete Ordinates Transport (DORT) two dimensional deterministic code. The analysis with proton beam energy of 1.3 GeV showed that the dose rate in continuously occupied areas on the lateral surface outside the SNS target station shielding monolith is less than 0.25 mrem/h, which complies with the SNS facility design objective. However, the methods and codes used in this analysis are out of date and unsupported, and the 2D approximation of the target shielding monolith does not accurately represent the geometry. We recommend that this analysis is updated with modern codes and libraries such as ADVANTG or SHIFT. These codes have demonstrated very high efficiency in performing full 3D radiation shielding analyses of similar and even more difficult problems.

42 ENGINEERING↗

DOE OSTI2016

Comparing Thick Target Neutron Yield Calculations for Alphas on F-19 using MCNP6 1.1 Beta with TENDL 2012 Libraries

Because of the heritage of MCNPX, the first production release of MCNP6{sup TM} used physics models, instead of ACE (A Compact Evaluated Nuclear Data File) formatted libraries, for simulating the transport of alpha particles at all energies, as MCNPX could not yet read ACE formatted light ion libraries. For non-Coulombic interactions, the physics models have suggested minimums of ∼ 20-200 MeV. The first production release of MCNP6, MCNP6{sup TM} 1.0, was still capable of transporting alphas to low energies; however, the physics models resulted in poor prediction of inelastic reactions at energies below the suggested minimums. This poorer prediction at low energies resulted in orders of magnitude lower neutron yields for events resulting from light ion interactions at low energies. For example, F-19(α,n)Na-22 from U-234 decay in UF{sub 6} produced poor neutron production results. Modeling these types of interactions correctly is vital for signal analysis for nonproliferation safeguards and nuclear security as well as for correctly characterizing source terms for radiation protection and shielding calculations. The TALYS computer code was developed to simulate nuclear reactions involving neutrons, photons, protons, deuterons, tritons, alphas and He-3 particles, in the 1 keV - 200 MeV energy range. TENDL is a nuclear data library, which provides the output of the TALYS nuclear model code system for direct use in applications (i.e. ACE formatted cross sections). Since 2008, TENDL had publicly provided ACE formatted libraries for alpha transport; however, not until the release of MCNP6{sup TM} 1.1 beta, had MCNP6 publicly possessed the capability to read such libraries. At the Radiation Protection and Shielding Division topical meeting in 2014, initial testing of MCNP6 1.1 beta showed that MCNP6 using TENDL 2012 resulted in poor prediction when compared to the compilation of experiment data provided in R. Heaton et al. Further comparisons for PuBe sources, using the MCNP6 1.1 beta spontaneous decay alpha feature coupled with alpha transport, were also given in the winter American Nuclear Society meeting in 2014, where the results showed large discrepancies with measured data. For F-19(α,n)Na-22 interactions, the R. Heaton et al. paper is a conglomeration of several important references; and the data provided by R. Heaton et al. used a numerical fit of the excitation function provided by Norman et al.. Norman et al. has recently published their (α,n) thick target yield data for PbF{sub 2} in tabular form, backing out the thick target yields of F-19(α,n)Na-22 and computing F{sub 2} and UF{sub 6} thick target neutron yields (as supplements to the PbF{sub 2} data). Here we compared measured thick target yields of E. B. Norman et al. to MCNP6 1.1 beta using TENDL 2012 libraries. Deterministically backing out the thick target yield from measured values involves using the stopping power. MCNP uses either the SPAR code or an interpolation of the Spar code and Bethe-Boch formula for computing stopping powers. As a result one may believe that these separate methods will arrive at separate thick target yields independent of the production cross section. In our MCNP calculations, we simulated the alpha until the alpha completely stopped in the medium. The energy loss in the condensed history algorithm is not continuous but discrete and related to the stopping powers. Because the neutron production yield is energy dependent, the energy loss dictates the emission of secondary neutrons; and therefore the difference in stopping powers used may have some slight effect on the yield difference. For this study, we chose to use the default condensed history algorithm setup, while future studies should address tightening steps in the condensed history algorithm to prove whether or not choice of stopping power actually matters. The fact that all 5 evaluated isotopic compositions resulted in different thick target yield percent differences does warrant this type of investigation. A typical user may not be only interested in integral emission, taking 10 s to 100 s of processor hours to simulate, but rather a timely generation of the spectra. Such an application would require biasing the production of secondaries from alpha sources such that millions of secondaries could be produced, emulating a converged energy dependent spectra. Neutron production biasing from alpha sources does not currently exist; however, developing a biasing algorithm for neutron production from alpha sources should be investigated in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Neutron Number Probability Distributions in a Subcritical System Using the Forward Master Equation

The subject of this paper is the determination of the equilibrium probability distribution function (PDF) of the neutron population within a subcritical multiplying system in the presence of an intrinsic random source as a result of spontaneous fission (SF). Such systems are strongly stochastic and low order moments such as the mean and variance of the neutron number are not sufficient to characterize the true state of the neutron population. We base our work on the forward Master equation in a lumped model formulation for P{sub n} (t), n = 0, 1, 2,..., the probability of there existing η neutrons at time t and obtain solutions for the steady state distributions. The key parameters in this model, neutron lifetime and fission probability, are rigorously obtained by numerically solving the k-eigenvalue problem in a one-dimensional spherical system and using standard definitions of lumped parameters in terms of production and loss rates in finite systems. Previous work has examined single-chain PDFs (a single fission and the resulting progeny) for neutrons, uncoupled neutron-photon single-chain PDFs, both using a lumped backward Master equation formulation, as well as neutron PDFs in supercritical systems. In this paper, we analyze strictly subcritical systems, for which we compare analytically derived solutions with numerical computations for different multiplicity distribution models. For subcritical systems where the neutron population remains finite at steady state, we show that the forward Master equation can be very efficiently solved numerically to obtain the equilibrium distributions. Finally, this work is motivated by potential application to passive interrogation for detection of diverted nuclear materials as well to criticality safety. The equilibrium neutron number probability distribution in subcritical systems has been numerically investigated for several models of the induced and spontaneous fission multiplicity distribution. A well known approximate but closed form solution, namely the quadratic model, was shown to be unacceptably accurate for subcritical media. The matrix inversion and the recursion methods agree well and were benchmarked with the binary fission model, but the recursion formulation proves far more computationally efficient. A qualitative change in the shape of the neutron PDF is observed for increasing source strength, as expected, and a transition from a stochastic to a deterministic system is expected for a sufficiently strong source. We are currently extending this approach to obtain the equilibrium joint distribution of neutrons within the system and those that have leaked, to explicitly represent the neutron PDF as observed by an external detector. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques

Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.

Geochemistry & Geophysics↗

DOE OSTI2020

MOCMC: Method of Characteristics Moment Closure, a Numerical Method for Covariant Radiation Magnetohydrodynamics

In this work, we present a conservative numerical method for radiation magnetohydrodynamics with frequency-dependent full transport in stationary spacetimes. This method is stable and accurate for both large and small optical depths and radiation pressures. The radiation stress–energy tensor is evolved in flux-conservative form, and closed with a swarm of samples that each transport a multigroup representation of the invariant specific intensity along a null geodesic. In each zone, the enclosed samples are used to efficiently construct a Delaunay triangulation of the unit sphere in the comoving frame, which in turn is used to calculate the Eddington tensor, average source terms, and adaptively refine the sample swarm. Furthermore, radiation four-forces are evaluated in the moment sector in a semi-implicit fashion. The radiative transfer equation is solved in invariant form deterministically for each sample. Since each sample carries a discrete representation of the full spectrum, the cost of evaluating the transport operator is independent of the number of frequency groups, representing a significant reduction of algorithmic complexity for transport in frequency-dependent problems. The major approximation we make in this work is performing scattering in an angle-averaged way. Local adaptivity in samples also makes this scheme more amenable to nonuniform meshes than a traditional Monte Carlo method. We describe the method and present results on a suite of test problems. We find that Method of Characteristics Moment Closure converges at least as ~N -1 , rather than the canonical Monte Carlo N -1/2 , where N is the number of samples per zone.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Hybrid framework for the simulation of stochastic chemical kinetics

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Simulation and Non-Simulation Based Human Reliability Analysis Approaches

Part of the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, the Risk-Informed Safety Margin Characterization (RISMC) Pathway develops approaches to estimating and managing safety margins. RISMC simulations pair deterministic plant physics models with probabilistic risk models. As human interactions are an essential element of plant risk, it is necessary to integrate human actions into the RISMC risk model. In this report, we review simulation-based and non-simulation-based human reliability assessment (HRA) methods. Chapter 2 surveys non-simulation-based HRA methods. Conventional HRA methods target static Probabilistic Risk Assessments for Level 1 events. These methods would require significant modification for use in dynamic simulation of Level 2 and Level 3 events. Chapter 3 is a review of human performance models. A variety of methods and models simulate dynamic human performance; however, most of these human performance models were developed outside the risk domain and have not been used for HRA. The exception is the ADS-IDAC model, which can be thought of as a virtual operator program. This model is resource-intensive but provides a detailed model of every operator action in a given scenario, along with models of numerous factors that can influence operator performance. Finally, Chapter 4 reviews the treatment of timing of operator actions in HRA methods. This chapter is an example of one of the critical gaps between existing HRA methods and the needs of dynamic HRA. This report summarizes the foundational information needed to develop a feasible approach to modeling human interactions in the RISMC simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Design criteria for XeF{sub 2} enabled deterministic transformation of bulk silicon (100) into flexible silicon layer

Isotropic etching of bulk silicon (100) using Xenon Difluoride (XeF{sub 2}) gas presents a unique opportunity to undercut and release ultra-thin flexible silicon layers with pre-fabricated state-of-the-art Complementary Metal Oxide Semiconductor (CMOS) electronics. In this work, we present design criteria and mechanism with a comprehensive mathematical model for this method. We consider various trench geometries and parametrize important metrics such as etch time, number of cycles and area efficiency in terms of the trench diameter and spacing so that optimization can be done for specific applications. From our theoretical analysis, we conclude that a honeycomb-inspired hexagonal distribution of trenches can produce the most efficient release of ultra-thin flexible silicon layers in terms of the number of etch cycles, while a rectangular distribution of circular trenches provides the most area efficient design. The theoretical results are verified by fabricating and releasing (varying sizes) flexible silicon layers. We observe uniform translation of design criteria into practice for etch distances and number of etch cycles, using reaction efficiency as a fitting parameter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

An explicitly solvable energy-conserving algorithm for pitch-angle scattering in magnetized plasmas

In this work, we develop an Explicitly Solvable Energy-Conserving (ESEC) algorithm for the Stochastic Differential Equation (SDE) describing the pitch-angle scattering process in magnetized plasmas. The Cayley transform is used to calculate both the deterministic gyromotion and stochastic scattering, affording the algorithm to be explicitly solvable and exactly energy conserving. An unusual property of the SDE for pitch-angle scattering is that its coefficients diverge at the zero velocity and do not satisfy the global Lipschitz condition. Consequently, when standard numerical methods, such as the Euler-Maruyama (EM), are applied, numerical convergence is difficult to establish. For the proposed ESEC algorithm, its energy-preserving property enables us to overcome this obstacle. We rigorously prove that the ESEC algorithm is order 1/2 strongly convergent. This result is confirmed by detailed numerical studies. For the case of pitch-angle scattering in a magnetized plasma with a constant magnetic field, the numerical solution is benchmarked against the analytical solution, and excellent agreements are found.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests

Accurate and reliable near-real-time satellite precipitation estimation is of great importance for operational large-scale flood forecasting and drought monitoring. The state-of-the-art precipitation post-processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P-NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near-real-time predictors to further improve the model performance. Using the Integrated Multi-satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG-E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG-E and is also superior to the bias-corrected product (IMERG Final Run, IMERG-F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG-E, raw IMERG-E, and IMERG-F using ground observation show that the corrected IMERG-E improves correlation coefficients (0.7), mean error (-0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG-E (0.31, -0.72 and 5.5 mm/day) and IMERG-F (0.34, -0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P-NRT improves on the various deficiencies of the raw IMERG-E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post-processing for near-real-time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The electronic heat capacity of YBa{sub 2}Cu{sub 3}O{sub 7−δ} superconductor

The contributions due to the point defects or disorder and anharmonicities which play deterministic role in the understanding of electronic heat capacity (EHC) of high temperature superconductors (HTS) have been investigated via electron density of states (EDOS) approach on the basis of quantum dynamical many body theory. The evaluation of EDOS has been carried out with the help of most versatile method of double time temperature dependent electron Green’s functions (GF) via a Hamiltonian (non BCS type) which includes the effects of electrons, phonons, defects, anharmonicity, and electron-phonon interactions which enables to account the effects of cubic anharmonicity besides with both the force constant changes and mass difference caused by the impurities in developing the results for EDOS and EHC. The new results reveal some striking features of EHC of HTS.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

High-level coupled-cluster energetics by Monte Carlo sampling and moment expansions: Further details and comparisons

We recently proposed a novel approach to converging electronic energies equivalent to high-level coupled-cluster (CC) computations by combining the deterministic CC(P;Q) formalism with the stochastic configuration interaction (CI) and CC Quantum Monte Carlo (QMC) propagations. This article extends our initial study [J. E. Deustua, J. Shen, and P. Piecuch, Phys. Rev. Lett. 119, 223003 (2017)], which focused on recovering the energies obtained with the CC method with singles, doubles, and triples (CCSDT) using the information extracted from full CI QMC and CCSDT-MC, to the CIQMC approaches truncated at triples and quadruples. It also reports our first semi-stochastic CC(P;Q) calculations aimed at converging the energies that correspond to the CC method with singles, doubles, triples, and quadruples (CCSDTQ). The ability of the semi-stochastic CC(P;Q) formalism to recover the CCSDT and CCSDTQ energies, even when electronic quasi-degeneracies and triply and quadruply excited clusters become substantial, is illustrated by a few numerical examples, including the F–F bond breaking in F 2 , the automerization of cyclobutadiene, and the double dissociation of the water molecule.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Dynamical structure factors of warm dense matter from time-dependent orbital-free and mixed-stochastic-deterministic density functional theory

Abstract We present the first calculations of the inelastic part of the dynamical structure factor (DSF) for warm dense matter (WDM) using time-dependent orbital-free density functional theory (TD-OF-DFT) and mixed-stochastic-deterministic (mixed) Kohn Sham TD-DFT (KS TD-DFT). WDM is an intermediate phase of matter found in planetary cores and laser-driven experiments, where the accurate calculation of the DSF is critical for interpreting x-ray Thomson scattering measurements. Traditional TD-DFT methods, while highly accurate, are computationally expensive, motivating the exploration of TD-OF-DFT and mixed TD-KS-DFT as more efficient alternatives. We applied these methods to experimentally measured WDM systems, including solid-density aluminum and beryllium, compressed beryllium, and carbon–hydrogen mixtures. Our results show that TD-OF-DFT requires a dynamical kinetic energy potential in order to qualitatively capture the plasmon response. Additionally, it struggles with capturing bound electron contributions. In contrast, mixed TD-KS-DFT offers greater accuracy in distinguishing bound and free electron effects, aligning well with experimental data, though at a higher computational cost. This study highlights the trade-offs between computational efficiency and accuracy, demonstrating that TD-OF-DFT remains a valuable tool for rapid scans of parameter space, while mixed TD-KS-DFT should be preferred for high-fidelity simulations. Our findings provide insight into the future development of DFT methods for WDM and suggest potential improvements for TD-OF-DFT.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Qumode transfer between continuous- and discrete-variable devices

Transferring quantum information between different types of quantum hardware is crucial for integrated quantum technology. In particular, converting information between continuous variable (CV) and discrete variable (DV) devices enables many applications in quantum networking, quantum sensing, quantum machine learning, and quantum computing. This paper addresses the transfer of CV-encoded information between CV and DV devices. We present a resource-efficient method for encoding CV states and implementing CV gates on DV devices, as well as two measurement-based protocols for transferring CV states between CV and DV devices. The success probability of the transfer protocols depends on the measurement outcome and can be increased to near-deterministic values by adding ancillary qubits to the DV devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally-heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints, this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole's inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

HIGH-FIDELITY SIMULATION-DRIVEN MODEL DEVELOPMENT FOR COARSE-GRAINED COMPUTATIONAL FLUID DYNAMICS

Nuclear reactor safety analysis requires identifying various credible accident scenarios and determining their consequences. For a full-scale nuclear power plant system behavior, it is impossible to obtain sufficient experimental data for a broad range of risk-significant accident scenarios. In single-phase flow convective problems, Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) can provide us with high fidelity results when physical data are unavailable. However, these methods are computationally expensive and cannot be afforded for simulation of long transient scenarios in nuclear accidents despite extraordinary advances in high performance scientific computing over the past decades. The major issue is the inability to make the transient computation parallel, thus making number of time steps required in high-fidelity methods unaffordable for long transients. In this work, we propose to apply a high fidelity simulation-driven approach to model sub-grid scale (SGS) effect in Coarse Grained Computational Fluid Dynamics CG-CFD. This approach aims to develop a statistical surrogate model instead of the deterministic SGS model. We chose to start with a turbulent natural convection case with volumetric heating in a horizontal fluid layer with a rigid, insulated lower boundary and isothermal (cold) upper boundary. This scenario of unstable stratification is relevant to turbulent natural convection in a molten corium pool during a severe nuclear reactor accident, as well as in containment mixing and passive cooling. The presented approach demonstrates how to create a correction for the CG-CFD solution by modifying the energy balance equation. A global correction for the temperature equation proves to achieve a significant improvement to the prediction of steady state temperature distribution through the fluid layer.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Direct SN Treatment of the Scattering Source with Phase Functions

In deterministic transport, the scattering operator is commonly expressed using a spherical harmonics (pn) expansion of the scattering source. Not only does this require minimal modifications if the problem dimensionality is changed, but it also contains little to no approximation whenever the scattering anisotropy degree is small. An alternative method relying on a discrete ordinates (sn) approach -- essentially computing the angular integral of the phase function with a finite number of weighted directions -- is theoretically possible and has shown some advantages. In particular, this method was introduced for neutron elastic scattering and was shown to be more accurate for highly anisotropic group-to-group transfer cross-sections. Benefits were further shown in the case of a collimated beam in highly forward peaked scattering media, particularly reducing the memory usage by storing an angle-to-angle collision matrix rather than a large number of angular moments. In addition, the scattering source was expressed using that same method, the phase function having been previously expanded using Legendre polynomials, in the context of achieving extreme precision while solving the 1D radiative transfer equation. Based on these previous works, we aim at exploring two questions. First, we wish to derive an expression of the phase function in reduced geometries that does not require any more angular quadrature points than for a standard sn calculation. This is particularly relevant in 2-D problems where the reduced phase function does not solely depend on the product of the incoming and outgoing directions. Second, we wish to better understand the relationship between the sn and pn treatments. We in particular show equivalence if the phase function in the former case is expanded Legendre polynomials. This equivalence is verified numerically and the computational costs of the methods are compared on a test problem involving a Dirac delta phase function.

97 MATHEMATICS AND COMPUTING↗