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DOE OSTI2012

Sparsity-Based Space-Time Adaptive Processing Using OFDM Radar

We propose a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly-moving target using an orthogonal frequency division multiplexing (OFDM) radar. We observe that the target and interference spectra are inherently sparse in the spatio-temporal domain, and hence we exploit that sparsity to develop an efficient STAP technique. In addition, the use of an OFDM signal increases the frequency diversity of our system, as different scattering centers of a target resonate at different frequencies, and thus improves the target detectability. First, we formulate a realistic sparse-measurement model for an OFDM radar considering both the clutter and jammer as the interfering sources. Then, we show that the optimal STAP-filter weight-vector is equal to the generalized eigenvector corresponding to the minimum generalized eigenvalue of the interference and target covariance matrices. To estimate the target and interference covariance matrices, we apply a residual sparse-recovery technique that enables us to incorporate the partially known support of the sparse vector. Our numerical results demonstrate that the sparsity-based STAP algorithm, with considerably lesser number of secondary data, produces an equivalent performance as the other existing STAP techniques.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Singular value decomposition utilizing parallel algorithms on graphical processors

One of the current challenges in underwater acoustic array signal processing is the detection of quiet targets in the presence of noise. In order to enable robust detection, one of the key processing steps requires data and replica whitening. This, in turn, involves the eigen-decomposition of the sample spectral matrix, Cx = 1/K xKX(k)XH(k) where X(k) denotes a single frequency snapshot with an element for each element of the array. By employing the singular value decomposition (SVD) method, the eigenvectors and eigenvalues can be determined directly from the data without computing the sample covariance matrix, reducing the computational requirements for a given level of accuracy (van Trees, Optimum Array Processing). (Recall that the SVD of a complex matrix A involves determining V, , and U such that A = U VH where U and V are orthonormal and is a positive, real, diagonal matrix containing the singular values of A. U and V are the eigenvectors of AAH and AHA, respectively, while the singular values are the square roots of the eigenvalues of AAH.) Because it is desirable to be able to compute these quantities in real time, an efficient technique for computing the SVD is vital. In addition, emerging multicore processors like graphical processing units (GPUs) are bringing parallel processing capabilities to an ever increasing number of users. Since the computational tasks involved in array signal processing are well suited for parallelization, it is expected that these computations will be implemented using GPUs as soon as users have the necessary computational tools available to them. Thus, it is important to have an SVD algorithm that is suitable for these processors. This work explores the effectiveness of two different parallel SVD implementations on an NVIDIA Tesla C2050 GPU (14 multiprocessors, 32 cores per multiprocessor, 1.15 GHz clock - peed). The first algorithm is based on a two-step algorithm which bidiagonalizes the matrix using Householder transformations, and then diagonalizes the intermediate bidiagonal matrix through implicit QR shifts. This is similar to that implemented for real matrices by Lahabar and Narayanan ("Singular Value Decomposition on GPU using CUDA", IEEE International Parallel Distributed Processing Symposium 2009). The implementation is done in a hybrid manner, with the bidiagonalization stage done using the GPU while the diagonalization stage is done using the CPU, with the GPU used to update the U and V matrices. The second algorithm is based on a one-sided Jacobi scheme utilizing a sequence of pair-wise column orthogonalizations such that A is replaced by AV until the resulting matrix is sufficiently orthogonal (that is, equal to U ). V is obtained from the sequence of orthogonalizations, while can be found from the square root of the diagonal elements of AH A and, once is known, U can be found from column scaling the resulting matrix. These implementations utilize CUDA Fortran and NVIDIA's CUB LAS library. The primary goal of this study is to quantify the comparative performance of these two techniques against themselves and other standard implementations (for example, MATLAB). Considering that there is significant overhead associated with transferring data to the GPU and with synchronization between the GPU and the host CPU, it is also important to understand when it is worthwhile to use the GPU in terms of the matrix size and number of concurrent SVDs to be calculated.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Updating and extending the IRDF-2002 dosimetry library

The International Reactor Dosimetry File (IRDF)-2002 released in 2004 by the IAEA (see http://www-nds.iaea.org/irdf2002/) contains cross-section data and corresponding uncertainties for 66 dosimetry reactions. New cross-section evaluations have become available recently that re-define some of these dosimetry reactions including: (1) high-fidelity evaluation work undertaken by one of the authors (KIZ); (2) evaluations from the US ENDF/B-VII.0 and candidate evaluations from the US ENDF/B-VII.1 libraries that cover reactions within the International Evaluation of Neutron Cross-Section Standards; (3) European JEFF3.1 library; and (4) Japanese JENDL-4.0 library. Additional high-threshold reactions not included in IRDF-2002 (e.g., {sup 59C}o(n,3n) and {sup 209}Bi(n,3n)) have been also evaluated to characterize higher-energy neutron fields. Overall, 37 new evaluations of dosimetry reactions have been assessed and intercomparisons made with integral measurements in reference neutron fields to determine whether they should be adopted to update and improve IRDF-2002. Benchmark calculations performed for newly evaluated reactions using the ENDF/B-VII.0 {sup 235}U thermal fission and {sup 252}Cf spontaneous fission neutron spectra show that calculated integral cross sections exhibit improved agreement with evaluated experimental data when compared with the equivalent data from the IRDF-2002 library. Data inconsistencies or deficiencies of new evaluations have been identified for {sup 63}Cu(n,2n), {sup 60}Ni(n,p) {sup 60m+g}Co, {sup 55}Mn(n,{gamma}), and {sup 232}Th(n,f) reactions. Compared with IRDF-2002, the upper neutron energy boundary was formally increased from the actual maximum energy of typically 20 MeV up to 60 MeV by using the TENDL-2010 cross sections and covariance matrices. This extension would allow the updated IRDF library to be also used in fusion dosimetry applications. Uncertainties in the cross sections for all new evaluations are given in the form of relative covariance matrices. Newly evaluated excitation functions should be considered as suitable candidates in the preparation of an improved version of the IRDF that was planned to be released for testing in December 2011. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2012

An efficient implementation of the Chebyshev Rational Approximation Method (CRAM) for solving the burnup equations

The Chebyshev Rational Approximation Method (CRAM) has been recently introduced by the authors for solving the burnup equations with excellent results. This method has been shown to be capable of simultaneously solving an entire burnup system with thousands of nuclides both accurately and efficiently. The method was prompted by an analysis of the spectral properties of burnup matrices and it can be characterized as the best rational approximation on the negative real axis. The coefficients of the rational approximation are fixed and have been reported for various approximation orders. In addition to these coefficients, implementing the method only requires a linear solver. This paper describes an efficient method for solving the linear systems associated with the CRAM approximation. The introduced direct method is based on sparse Gaussian elimination where the sparsity pattern of the resulting upper triangular matrix is determined before the numerical elimination phase. The stability of the proposed Gaussian elimination method is discussed based on considering the numerical properties of burnup matrices. Suitable algorithms are presented for computing the symbolic factorization and numerical elimination in order to facilitate the implementation of CRAM and its adoption into routine use. The accuracy and efficiency of the described technique are demonstrated by computing the CRAM approximations for a large test case with over 1600 nuclides. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

Sensitivity analysis of Monju using ERANOS with JENDL-4.0

This paper deals with sensitivity analysis using JENDL-4.0 nuclear data applied to the Monju reactor. In 2010 the Japan Atomic Energy Agency - JAEA - released a new set of nuclear data: JENDL-4.0. This new evaluation is expected to contain improved data on actinides and covariance matrices. Covariance matrices are a key point in quantification of uncertainties due to basic nuclear data. For sensitivity analysis, the well-established ERANOS [1] code was chosen because of its integrated modules that allow users to perform a sensitivity analysis of complex reactor geometries. A JENDL-4.0 cross-section library is not available for ERANOS. Therefore a cross-section library had to be made from the original nuclear data set, available as ENDF formatted files. This is achieved by using the following codes: NJOY, CALENDF, MERGE and GECCO in order to create a library for the ECCO cell code (part of ERANOS). In order to make sure of the accuracy of the new ECCO library, two benchmark experiments have been analyzed: the MZA and MZB cores of the MOZART program measured at the ZEBRA facility in the UK. These were chosen due to their similarity to the Monju core. Using the JENDL-4.0 ECCO library we have analyzed the criticality of Monju during the restart in 2010. We have obtained good agreement with the measured criticality. Perturbation calculations have been performed between JENDL-3.3 and JENDL-4.0 based models. The isotopes {sup 239}Pu, {sup 238}U, {sup 241}Am and {sup 241}Pu account for a major part of observed differences. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

Accuracy considerations for Chebyshev rational approximation method (CRAM) in Burnup calculations

The burnup equations can in principle be solved by computing the exponential of the burnup matrix. However, due to the difficult numerical characteristics of burnup matrices, the problem is extremely stiff and the matrix exponential solution has previously been considered infeasible for an entire burnup system containing over a thousand nuclides. It was recently discovered by the author that the eigenvalues of burnup matrices are generally located near the negative real axis, which prompted introducing the Chebyshev rational approximation method (CRAM) for solving the burnup equations. CRAM can be characterized as the best rational approximation on the negative real axis and it has been shown to be capable of simultaneously solving an entire burnup system both accurately and efficiently. In this paper, the accuracy of CRAM is further studied in the context of burnup equations. The approximation error is analyzed based on the eigenvalue decomposition of the burnup matrix. It is deduced that the relative accuracy of CRAM may be compromised if a nuclide concentration diminishes significantly during the considered time step. Numerical results are presented for two test cases, the first one representing a small burnup system with 36 nuclides and the second one a full a decay system with 1531 nuclides. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

A fission matrix based validation protocol for computed power distributions in the advanced test reactor

The Idaho National Laboratory (INL) has been engaged in a significant multi year effort to modernize the computational reactor physics tools and validation procedures used to support operations of the Advanced Test Reactor (ATR) and its companion critical facility (ATRC). Several new protocols for validation of computed neutron flux distributions and spectra as well as for validation of computed fission power distributions, based on new experiments and well-recognized least-squares statistical analysis techniques, have been under development. In the case of power distributions, estimates of the a priori ATR-specific fuel element-to-element fission power correlation and covariance matrices are required for validation analysis. A practical method for generating these matrices using the element-to-element fission matrix is presented, along with a high-order scheme for estimating the underlying fission matrix itself. The proposed methodology is illustrated using the MCNP5 neutron transport code for the required neutronics calculations. The general approach is readily adaptable for implementation using any multidimensional stochastic or deterministic transport code that offers the required level of spatial, angular, and energy resolution in the computed solution for the neutron flux and fission source. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Nuclear Data Uncertainty Quantification for Adjoint-Weighted Quantities via XGPT in Monte Carlo Codes

The Total Monte Carlo method (TMC) is an innovative approach to nuclear data uncertainty propagation. In TMC, a large number of independent ENDF files are randomly generated starting from nuclear models parameters and their uncertainties. These files are then processed with NJOY or similar codes to produce a set of formatted continuous-energy cross sections (i.e., ACE files), and independent Monte Carlo neutron transport simulations are run with these different ACE files as input. Finally the distributions of the considered response functions are obtained directly from the results of the Monte Carlo runs. Despite several improvements of the TMC method, this approach is often considered an inefficient way of propagating nuclear data uncertainties, as it requires several computationally expensive Monte Carlo runs. Covariance-based methods present important advantages for several applications, especially when different sources of uncertainties are to be investigated. For this reason, sensitivity analysis and uncertainty quantification capabilities are being implemented in several Monte Carlo neutron transport codes. On the other hand, the fact that no artificial multi-group covariance matrices are required and that higher moments of the responses distributions can be easily obtained, are definite advantages of the TMC method. In an effort to overcome the limitations of both approaches, a new method, called eXtended Generalized Perturbation Theory (XGPT) is under development. When performing nuclear data uncertainty propagation, such XGPT approach does not require generating discretized (i.e., multigroup) covariance matrices, and allows to estimate higher moments of the response probability distributions, via linear perturbation theory. One of the promising applications of the XGPT method involves its adoption in conjunction with the Proper Orthogonal Decomposition (POD) of nuclear data, to build a Reduced Order Model (ROM) for the approximation of thousands of independent TMC results in a single criticality source simulation. In the present work, this approach is employed for the first time to evaluate the impact of uncertainty in nuclear data on adjoint-weighted quantities. The method has been implemented in an extended version of the Serpent-2.1.24 Monte Carlo code and is demonstrated on two fast spectrum benchmarks, focusing on {sup 208}Pb nuclear data. A new method for propagating nuclear data uncertainties in criticality source Monte Carlo simulations has been developed. This eXtended Generalized Perturbation Theory (XGPT) approach allows to accurately reproduce reference Total Monte Carlo (TMC) uncertainty estimates in a single criticality source simulation. The present work aims to demonstrate that the proposed method can quantify nuclear data uncertainty based on adjoint-weighted quantities. The XGPT approach was applied to evaluate the impact of {sup 208}Pb nuclear data uncertainties on the effective prompt neutron lifetime (l{sub eff}) in two fast spectrum systems. The obtained XGPT results show a good agreement with the reference TMC results, obtained from independent Monte Carlo runs with {sup 208}Pb nuclear data from random TENDL-2013 evaluations. The XGPT method, implemented in a extended version of the Serpent Monte Carlo code and adopted along with the Proper Orthogonal Decomposition (POD) of the nuclear data, is able to provide an accurate reduced order model to approximate thousands TMC results within a single run, ensuring a large saving in computational requirements. The effect of {sup 208}Pb nuclear data uncertainties appears to be rather large. This is consistent with previous studies that adopted the same input random evaluations data from TENDL. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

The Iterated Fission Matrix Method

The fission matrix (FM) method has been successfully demonstrated for different applications. One of the interesting features of this method, which is not possible via standard criticality source Monte Carlo calculations, is the capability to calculate higher forward and adjoint Eigenmodes; however, the accuracy of the estimates for higher eigenvalues strongly depends on the spatial discretization adopted for the fission matrix. A fine mesh ensures accurate results but requires matrices of large size. This is considered a limitation to the applicability of the FM method due to the memory requirements for storage of large full matrices. The adoption of sparse matrix storage (e.g., in MCNP) partially overcomes this problem; nonetheless, the sparse matrix indexing increases the complexity of the implementation of the FM algorithm and introduces the additional task of sparsifying the fission matrix. This summary presents a new method, named the iterated fission matrix (IFM), that allows to estimate higher eigenvalues of multiplying systems on much smaller meshes, but with the same level of accuracy in terms of spatial discretization errors, as compared to the FM method. The following Sections describe this new approach and provide a demonstration using two case studies: a PWR fuel assembly, and a 2D PWR core. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Variational Nodal 2D/1D Transport/Diffusion Solutions of the C5G7 Benchmark Problems

This work is the continuation of our effort to generalize the variational nodal method (VNM) for application to pin-resolved whole-core PWR problems. To this end, a 2D/1D VNM method without spatial homogenization is formulated and implemented in the PANX code. We employ diffusion theory in the axial direction combined with two-dimensional transport in the x-y plane. In our initial work, we employed the standard spherical harmonics method. To increase accuracy and reduce computational effort, we present two refinements: First, the even-parity integral method within the nodes reduces the computational effort required to form the response matrices. Second, the use of quasi-reflected interface conditions reduces the dimension of the resulting response matrices and therefore the CPU time required to solve the equations. We apply the method to the three-dimensional, un-rodded C5G7 benchmark problem. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Uncertainties in secondary angular distributions and their impact on shielding, criticality and kinetics analysis - 25556

The availability of the covariance data improved over the last decades. Use of covariance matrices of different origin (such as JEFF, JENDL, ENDF, TENDL, SCALE, etc.) and types (files MF31, MF33, MF34, MF35) in sensitivity and uncertainty analysis of vast list of cases ranging from fission to fusion and from criticality, kinetics and shielding to adjustment applications was already studied in the past. The present work focuses on the status and the availability of the covariance of the angular distributions of scattered neutrons (secondary angular distributions - SAD covariances). SAD uncertainties are usually neglected in classical uncertainty propagation codes, even though the methods and codes for calculating the sensitivities are available for decades and excellent agreement was demonstrated between different deterministic and Monte Carlo codes. Of particular concern is still the availability, quality and completeness of covariance matrices. Indeed, in most evaluations the SAD covariances are limited to P1 term, if available at all. Furthermore, some differences were observed between different evaluations, e.g. in the case of iron. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Polymer Zwitterions for Stabilization of CsPbBr 3 Perovskite Nanoparticles and Nanocomposite Films

Abstract Functional polymers with sulfobetaine or phosphorylcholine zwitterions as pendent groups are demonstrated as both ligands and host matrices for CsPbBr 3 perovskite nanoparticles (PNPs). These polymers produce nanocomposite films with excellent NP dispersion, optical transparency, and impressive resistance to NP degradation upon exposure to water. Multidentate interactions of the zwitterion‐containing copolymers with the PNPs induce dispersed or weakly aggregated nanocomposite morphologies, depending on the extent of zwitterionic functionality in the polymer. Incorporating additional functionality into the polymers, such as benzophenone pendent groups, yields lithographically patternable films, while time‐resolved photoluminescence measurements provide insight into the electronic impact of PNPs in zwitterionic polymer matrices.

Kim, Hyunki↗

DOE OSTI2024

Catalytically Active Site Mapping Realized through Energy Transfer Modeling

Abstract The demands of a sustainable chemical industry are a driving force for the development of heterogeneous catalytic platforms exhibiting facile catalyst recovery, recycling, and resilience to diverse reaction conditions. Homogeneous‐to‐heterogeneous catalyst transitions can be realized through the integration of efficient homogeneous catalysts within porous matrices. Herein, we offer a versatile approach to understanding how guest distribution and evolution impact the catalytic performance of heterogeneous host–guest catalytic platforms by implementing the resonance energy transfer (RET) concept using fluorescent model systems mimicking the steric constraints of targeted catalysts. Using the RET‐based methodology, we mapped condition‐dependent guest (re)distribution within a porous support on the example of modular matrices such as metal–organic frameworks (MOFs). Furthermore, we correlate RET results performed on the model systems with the catalytic performance of two MOF‐encapsulated catalysts used to promote CO 2 hydrogenation and ring‐closing metathesis. Guests are incorporated using aperture‐opening encapsulation, and catalyst redistribution is not observed under practical reaction conditions, showcasing a pathway to advance catalyst recyclability in the case of host–guest platforms. These studies represent the first generalizable approach for mapping the guest distribution in heterogeneous host–guest catalytic systems, providing a foundation for predicting and tailoring the performance of catalysts integrated into various porous supports.

Thompson, William J.↗

DOE OSTI2020

Polymer Zwitterions for Stabilization of CsPbBr 3 Perovskite Nanoparticles and Nanocomposite Films

Functional polymers with sulfobetaine or phosphorylcholine zwitterions as pendent groups are demonstrated as both ligands and host matrices for CsPbBr 3 perovskite nanoparticles (PNPs). These polymers produce nanocomposite films with excellent NP dispersion, optical transparency, and impressive resistance to NP degradation upon exposure to water. Multidentate interactions of the zwitterion-containing copolymers with the PNPs induce dispersed or weakly aggregated nanocomposite morphologies, depending on the extent of zwitterionic functionality in the polymer. Incorporating additional functionality into the polymers, such as benzophenone pendent groups, yields lithographically patternable films, while time-resolved photoluminescence measurements provide insight into the electronic impact of PNPs in zwitterionic polymer matrices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Catalytically Active Site Mapping Realized through Energy Transfer Modeling

Thompson, William J.↗

DOE OSTI2020

On‐site surrogates for large‐scale calibration

Abstract Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on‐site design and surrogate modeling for a computer simulator acting on a high‐dimensional input space that, although relatively speedy, is prone to numerical instabilities, missing data, and nonstationary dynamics. Our aim is to strike a balance between data‐faithful modeling and computational tractability in a calibration framework—tailoring the computer model to a limited field experiment. Situating our on‐site surrogates within the canonical calibration apparatus requires updates to that framework. We describe a novel yet intuitive Bayesian setup that carefully decomposes otherwise prohibitively large matrices by exploiting the sparse blockwise structure. Empirical illustrations demonstrate that this approach performs well on toy data and our motivating honeycomb example.

Huang, Jiangeng↗

DOE OSTI2015

Scalable Preparation of Ternary Hierarchical Silicon Oxide-Nickel-Graphite Composites for Lithium-Ion Batteries

Silicon monoxide is a promising anode candidate because of its high theoretical capacity and good cycle performance. To solve the problems associated with this material, including large volume changes during charge-discharge processes, we report a ternary hierarchical silicon oxide–nickel–graphite composite prepared by a facile two-step ball-milling method. The composite consists of nano-Si dispersed silicon oxides embedded in nano-Ni/graphite matrices (Si@SiOx/Ni/graphite). In the composite, crystalline nano-Si particles are generated by the mechanochemical reduction of SiO by ball milling with Ni. These nano-Si dispersed oxides have abundant electrochemical activity and can provide high Li-ion storage capacity. Furthermore, the milled nano-Ni/graphite matrices stick well to active materials and interconnect to form a crosslinked framework, which functions as an electrical highway and a mechanical backbone so that all silicon oxide particles become electrochemically active. Owing to these advanced structural and electrochemical characteristics, the composite enhances the utilization efficiency of SiO, accommodates its large volume expansion upon cycling, and has good ionic and electronic conductivity. The composite electrodes thus exhibit substantial improvements in electrochemical performance. This ternary hierarchical Si@SiOx/Ni/graphite composite is a promising candidate anode material for high-energy lithium-ion batteries. Additionally, the mechanochemical ball-milling method is low cost and easy to reproduce, indicating potential for the commercial production of the composite materials.

25 ENERGY STORAGE↗

DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗