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Determination of low-level biogenic gasoline, jet fuel, and diesel in blends using the direct liquid scintillation counting method for 14C content
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Resonance treatment using pin-based pointwise energy slowing-down method
A new resonance self-shielding method using a pointwise energy solution has been developed to overcome the drawbacks of the equivalence theory. The equivalence theory uses a crude resonance scattering source approximation, and assumes a spatially constant scattering source distribution inside a fuel pellet. These two assumptions cause a significant error, in that they overestimate the multi-group effective cross sections, especially for {sup 238}U. The new resonance self-shielding method solves pointwise energy slowing-down equations with a sub-divided fuel rod. The method adopts a shadowing effect correction factor and fictitious moderator material to model a realistic pointwise energy solution. The slowing-down solution is used to generate the multi-group cross section. With various light water reactor problems, it was demonstrated that the new resonance self-shielding method significantly improved accuracy in the reactor parameter calculation with no compromise in computation time, compared to the equivalence theory.
The Random Ray Method for neutral particle transport
A new approach to solving partial differential equations (PDEs) based on the method of characteristics (MOC) is presented. The Random Ray Method (TRRM) uses a stochastic rather than deterministic discretization of characteristic tracks to integrate the phase space of a problem. TRRM is potentially applicable in a number of transport simulation fields where long characteristic methods are used, such as neutron transport and gamma ray transport in reactor physics as well as radiative transfer in astrophysics. In this study, TRRM is developed and then tested on a series of exemplar reactor physics benchmark problems. The results show extreme improvements in memory efficiency compared to deterministic MOC methods, while also reducing algorithmic complexity, allowing for a sparser computational grid to be used while maintaining accuracy.
Liquidus temperature in the spinel primary phase field: A comparison between optical and crystal fraction methods
Liquidus temperature (TL) was measured for simulated high-level waste borosilicate glasses covering a Hanford composition region, using an optical method and a crystal-fraction extrapolation method with X-ray diffraction data from isothermal heat treatments. The 38 glasses encompassed a one-component-at-a-time variation of a 16-component matrix. The TL values ranged from 1006°C to 1603°C. First-order polynomial models were fitted to data to obtain coefficients in terms of the effect of 1 mass% component addition on the TL: TL-increasing oxides Cr2O3 (264°C), “Others” (minor components, 163°C), oxides of noble metals (137°C), NiO (91°C), Al2O3 and Fe2O3 (~19–21°C); TL-decreasing oxides K2O (-26°C), Na2O (-41°C), and Li2O (-68°C); oxides of little effect MnO, P2O5, ZrO2, F, Bi2O3, SiO2, B2O3, and CaO (9 to -12°C). Also presented are temperatures (T1%) at which glasses contain 1 vol% of spinel as these values are considered relevant to the Hanford Tank Waste Treatment and Immobilization Plant. The measured and estimated values are compared and contrasted and the effect of TL and T1% on glass formulation is discussed.
Approximate Green’s Function Coupled Cluster A Method Employing Effective Dimension Reduction
The Green’s function coupled cluster (GFCC) method, originally proposed in the early 1990s, is a powerful many-body tool for computing and analyzing the electronic structures of molecular structures and periodic systems, especially when electrons are strongly correlated. However, in order for the GFCC method to become an approach that can be routinely used in the electronic structure calculations, more robust linear algebraic techniques and approximations need to be employed to reduce its extremely high computational overhead. In our recent studies, we showed that the GFCC equation can be numerically solved by seeking solutions of multiple linear systems in the frequency regime of interest, which can be easily distributed in a massively parallel environment, and broaden the avenue for more state-of-the-art numerical linear algebraic tools associated with multiple linear systems to be kicked in to this field. In the present pursuit of this practice, we show a successful application of model order reduction (MOR) techniques in the GFCC framework. Briefly speaking, for a frequency regime of interest that requires a high resolution of spectral function, instead of solving GFCC linear equation in the original dimension for every single frequency point, an easily-solvable linear system model with reduced dimension can be built upon projecting the original GFCC linear system into a subspace, from which the interpolation and extrapolation can give a reasonable approximation to the results of the original GFCC linear equations. Here, we show that the subspace can be properly constructed in an iterative manner from the auxiliary vectors of the GFCC linear equations at some select frequencies inside the frequency regime. During the iterations, the quality of the subspace, as well as the linear system model, can be systematically improved. The approach is tested in this work in terms of the efficiency and accuracy of computing spectral functions for some typical molecular systems including carbon monoxide, 1,3-butadiene, benzene, and adenine molecules. As a byproduct, the high quality linear system model is also found to be able to provide high quality initial guess for the existing linear solver to improve the convergence performance.
IMPROVING THE ACCURACY OF COMPOSITE METHODS: A G4MP2 METHOD WITH G4-LIKE ACCURACY AND IMPLICATIONS FOR MACHINE LEARNING
G4MP2 theory has proven to be a reliable and accurate quantum chemical composite method for the calculation of molecular energies using an approximation based on second-order perturbation theory to lower computational costs compared to G4 theory. However, it has been found to have significantly increased errors when applied to larger organic molecules with 10 or more nonhydrogen atoms. We report here on an investigation of the cause of the failure of G4MP2 theory for such larger molecules. One source of error is found to be the "higher-level correction (HLC)", which is meant to correct for deficiencies in correlation contributions to the calculated energies. This is because the HLC assumes that the contribution is independent of the element and the type of bonding involved, both of which become more important with larger molecules. We address this problem by adding an atom-specific correction, dependent on atom type but not bond type, to the higher-level correction. We find that a G4MP2 method that incorporates this modification of the higher-level correction, referred to as G4MP2A, becomes as accurate as G4 theory (for computing enthalpies of formation) for a test set of molecules with less than 10 nonhydrogen atoms as well as a set with 10-14 such atoms, the set of molecules considered here, with a much lower computational cost. The G4MP2A method is also found to significantly improve ionization potentials and electron affinities. Finally, we implemented the G4MP2A energies in a machine learning method to predict molecular energies.
A high throughput optical method for studying compositional effects in electrocatalysts for CO2 reduction
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A novel method for measuring ultra-trace levels of U and Th in Au, Pt, Ir, and W matrices using ICP-QQQ-MS employing an O 2 reaction gas
A mass shift method using ICP-MS/MS with O 2 reaction gas is used for ultra-trace determinations of U and Th in samples containing W, Ir, Pt, and Au derived polyatomic interferants without the need for extensive chemical sample preprocessing.
Advanced Modeling and Simulation Methods for Evaluation of Thermal Neutron Scattering Materials
With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a sufficiently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.
A time-implicit numerical method and benchmarks for the relativistic Vlasov–Ampere equations
We present a time-implicit numerical method to solve the relativistic Vlasov–Ampere system of equations on a two dimensional phase space grid. The time-splitting algorithm we use allows the generalization of the work presented here to higher dimensions keeping the linear aspect of the resulting discrete set of equations. The implicit method is benchmarked against linear theory results for the relativistic Landau damping for which analytical expressions using the Maxwell-Jüttner distribution function are derived. We note that, independently from the shape of the distribution function, the relativistic treatment features collective behaviours that do not exist in the nonrelativistic case. The numerical study of the relativistic two-stream instability completes the set of benchmarking tests.
Generalized spectral method for near-field optical microscopy
Electromagnetic interaction between a sub-wavelength particle (the “probe”) and a material surface (the “sample”) is studied theoretically. The interaction is shown to be governed by a series of resonances corresponding to surface polariton modes localized near the probe. The resonance parameters depend on the dielectric function and geometry of the probe as well as on the surface reflectivity of the material. Calculation of such resonances is carried out for several types of axisymmetric probes: spherical, spheroidal, and pear-shaped. For spheroids, an efficient numerical method is developed, capable of handling cases of large or strongly momentum-dependent surface reflectivity. Application of the method to highly resonant materials, such as aluminum oxide (by itself or covered with graphene), reveals a rich structure of multi-peak spectra and nonmonotonic approach curves, i.e., the probe-sample distance dependence. These features also strongly depend on the probe shape and optical constants of the model. For less resonant materials such as silicon oxide, the dependence is weak, so that the spheroidal model is reliable. The calculations are done within the quasistatic approximation with radiative damping included perturbatively.
A Solar-assisted Voltage Optimization Method for Transmission Solar Network Power System
This paper proposes a new formulation and solution algorithm that uses transmission level solar inverters to address the security-constrained optimal power flow (SCOPF) problem. The goal is to stabilize voltage fluctuations in transmission networks in base case and contingency scenarios, by using bulk solar power plant with a minimal number of post-contingency corrections. To achieve this goal, a two-stage volt/var optimization method is proposed to first correct all voltage violations with the volt-var alternating current optimal power flow (ACOPF) algorithm for a base case. Then a linearized SCOPF volt-var control algorithm is proposed to identify the corrective actions for all potential voltage violations in all contingency scenarios. The proposed method was tested and validated on a modified IEEE 118-bus system with solar photovoltaic (PV) data.
Determination of Series Resistance in CdSeTe/CdTe Solar Cells by the Jsc–Voc Method
While commonly used in Si solar cell characterization, the Suns–Voc method of determining series resistance (Rs) is rarely used for CdTe and its alloys. However, it is advantageous relative to the slope method—commonly used in the CdTe community—because it measures series resistance at the maximum power point, at which a solar cell operates. Using the self-consistent predecessor to Suns–Voc, Jsc –Voc, , we determine Rs of two different CdSeTe/CdTe solar cell structures to be 0.96 and 2.72 Ωcm2, with repeated measurements showing little deviation