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DOE OSTI2013

Depletion GPT-free sensitivity analysis for reactor eigenvalue problems

This manuscript introduces a novel approach to solving depletion perturbation theory problems without the need to set up or solve the generalized perturbation theory (GPT) equations. The approach, hereinafter denoted generalized perturbation theory free (GPT-Free), constructs a reduced order model (ROM) using methods based in perturbation theory and computes response sensitivity profiles in a manner that is independent of the number or type of responses, allowing for an efficient computation of sensitivities when many responses are required. Moreover, the reduction error from using the ROM is quantified in the GPT-Free approach by means of a Wilks' order statistics error metric denoted the K-metric. Traditional GPT has been recognized as the most computationally efficient approach for performing sensitivity analyses of models with many input parameters, e.g. when forward sensitivity analyses are computationally intractable. However, most neutronics codes that can solve the fundamental (homogenous) adjoint eigenvalue problem do not have GPT capabilities unless envisioned during code development. The GPT-Free approach addresses this limitation by requiring only the ability to compute the fundamental adjoint. This manuscript demonstrates the GPT-Free approach for depletion reactor calculations performed in SCALE6 using the 7x7 UAM assembly model. A ROM is developed for the assembly over a time horizon of 990 days. The approach both calculates the reduction error over the lifetime of the simulation using the K-metric and benchmarks the obtained sensitivities using sample calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Proposal and application of ROM-Lasso method for sensitivity coefficient evaluation

We propose a novel method for evaluating sensitivity coefficients of neutronics parameters to cross sections, so-called the reduced-order modeling technique ROM-Lasso. In this method, cross sections of interest are randomly sampled, and corresponding perturbed core analyses are performed. Then, the sensitivity coefficient vector of the higher-level model is expanded via the active subspace bases obtained with the lower-level model whose dimensional complexity is smaller than that of the higher-level model, and the expansion coefficients are estimated by the Lasso regression. A unique feature of the ROM-Lasso method allows the use of different bases optimized for each neutronics parameter. We conducted a verification calculation for an accelerator-driven system and demonstrated that the ROM-Lasso method can reproduce the sensitivity coefficients with a much smaller number of forward calculations than the direct method. The proposed method can be used to practically evaluate sensitivity coefficients. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2003

Air Cargo Transportation Route Choice Analysis

Using a unique feature of air cargo transshipment data in the Northeast Asian region, this paper identifies the critical factors that determine the transshipment route choice. Taking advantage of the variations in the transport characteristics in each origin-destination airports pair, the paper uses a discrete choice model to describe the transshipping route choice decision made by an agent (i.e., freight forwarder, consolidator, and large shipper). The analysis incorporates two major factors, monetary cost (such as line-haul cost and landing fee) and time cost (i.e., aircraft turnaround time, including loading and unloading time, custom clearance time, and expected scheduled delay), along with other controls. The estimation method considers the presence of unobserved attributes, and corrects for resulting endogeneity by use of appropriate instrumental variables. Estimation results find that transshipment volumes are more sensitive to time cost, and that the reduction in aircraft turnaround time by 1 hour would be worth the increase in airport charges by more than $1000. Simulation exercises measures the impacts of alternative policy scenarios for a Korean airport, which has recently declared their intention to be a future regional hub in the Northeast Asian region. The results suggest that reducing aircraft turnaround time at the airport be an effective strategy, rather than subsidizing to reduce airport charges.

Obashi, Hiroshi↗

NASA NTRSDate not supplied

Coherence Analysis of the Space Launch System using Unsteady Pressure Sensitive Paint

Transonic buffet forces are a major source of unsteady loading on launch vehicles, thus requiring accurate estimation for efficient vehicle design. The state of the art in modeling these unsteady loads utilizes wind-tunnel tests where the fluctuating pressures are measured by pressure transducers (PTs) at discrete locations on a rigid buffet model. These pressures are then integrated over the surface of the vehicle to yield a series of orthogonal centerline loads called buffet forcing functions (BFFs). Typically, the PT layout aims at resolving the pressure correlation along the longitudinal axis of the vehicle. As a result, the distribution of azimuthal correlation and its impact on the estimated BFFs are not well known. To fill these gaps, extremely high-spatial-resolution uPSP data were collected for three different configurations of the Space Launch System in the NASA Ames Research Center 11-Foot Transonic Unitary Plan Wind Tunnel. The spatio-temporal behavior of the pressure correlation on these vehicles is analyzed and flow features of interest are investigated. It is shown that terminal shocks interacting with turbulence are a source of increased azimuthal coherence, especially when the shock develops at a junction. Vortex shedding off the forward attachment hardware that connects the core stage to the solid rocket boosters (SRBs) is the most severe buffet environment on the vehicle. The associated fluctuating pressures are shown to be highly coherent as far as the vehicle tail and up to 40 degrees away from the boosters. For selected areas of the vehicle, factoring the azimuthal coherence into the attenuation of discrete-measurements-based BFFs results in under prediction relative to the BFFs obtained from full integration of the uPSP data.

buffet↗

DOE OSTI2016

An Efficient Sampling-Based Method for Sensitivity and Uncertainty Analysis Through RAVEN

Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Automatic differentiation of advanced CFD codes for multidisciplinary design

Automated multidisciplinary design of aircraft and other flight vehicles requires the optimization of complex performance objectives with respect to a number of design parameters and constraints. The effect of these independent design variables on the system performance criteria can be quantified in terms of sensitivity derivatives which must be calculated and propagated by the individual discipline simulation codes. Typical advanced CFD analysis codes do not provide such derivatives as part of a flow solution; these derivatives are very expensive to obtain by divided (finite) differences from perturbed solutions. It is shown that sensitivity derivatives can be obtained accurately and efficiently using the ADIFOR source translator for automatic differentiation. In particular, it is demonstrated that the 3-D, thin-layer Navier-Stokes, multigrid flow solver called TLNS3D is amenable to automatic differentiation in the forward mode even with its implicit iterative solution algorithm and complex turbulence modeling. It is significant that by using computational differentiation, consistent discrete nongeometric sensitivity derivatives have been obtained from an aerodynamic 3-D CFD code in a relatively short time, e.g., O(man-week) not O(man-year).

Bischof, C.↗

DOE OSTI2026

Analysis of the electrical double layer using electrochemical X-ray photoelectron spectroscopy

The element-sensitivity of X-ray spectroscopies offers the potential to disentangle the individual chemistries of water, ions, and adsorbates at the electrode-electrolyte interface in an element-by-element manner. However, targeted experimental design is needed to establish interface-sensitive in situ X-ray spectroscopy in a realistic electrochemical environment. Here, we demonstrate how electrochemical X-ray photoelectron spectroscopy (EC-XPS) in the dip-and-pull geometry can be used to specifically probe the behavior of ions in the electrical double layer. Taking the case study of a polycrystalline Au foil in 50 mM KClO 4 electrolyte, we tracked the electrochemical response of interfacial K + cations across a broad potential range. We show how, in combination with modeling, key parameters such as the potential of zero charge (PZC), ion packing behavior, dielectric saturation, and the electrostatic potential decay in the double layer can be extracted from the data. Importantly, we also analyze how the experimental conditions and non-idealities can influence the results and put forward criteria for reliable experimentation and data analysis.

Dip-and-pull↗

DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗

DOE OSTI2017

Sensitivity Analysis of Neutron Multiplicity Counting Statistics Using First-Order Perturbation Theory and Application to a Subcritical Plutonium Metal Benchmark

It is frequently important to estimate the uncertainty and sensitivity of measured and computed detector responses in subcritical experiments and simulations. These uncertainties arise from the physical construction of the experiment, uncertainties in the transport parameters, and counting uncertainties. Perturbation theory enables sensitivity analysis (SA) and uncertainty quantification on integral quantities like detector responses. Here the aim of our work is to apply SA to the statistics of subcritical neutron multiplicity counting distributions. Current SA methods have only been applied to mean detector responses and the keff eigenvalue. For multiplicity counting experiments, knowledge of the higher-order counting moments and their uncertainties are essential for a complete SA. We apply perturbation theory to compute the sensitivity of neutron multiplicity counting moments to arbitrarily high order. Each moment is determined by solving an adjoint transport equation with a source term that is a function of the adjoint solutions for lower-order moments. This enables moments of arbitrarily high order to be sequentially determined, and it shows that each moment is sensitive to the uncertainties of all lower-order moments. To close our SA of the moments, we derive forward transport equations that are functions of the forward flux and lower-order moment adjoint fluxes. We verify our calculations for the first three moments by comparison with multiplicity counting measurements of a subcritical plutonium metal sphere. For the first three moments, the most influential parameters are ranked, and the validity of first-order perturbation theory is demonstrated by examining the series truncation error. This enables a detailed SA of subcritical multiplicity counting measurements of fissionable material based on transport theory.

42 ENGINEERING↗

DOE OSTI2015

A forward analysis on the applicability of tracer breakthrough profiles in revealing the pore structure of tight gas sandstone and carbonate rocks

Here, we explore tracer breakthrough profiles (TBP) as a macroscopic property to infer the pore-space topology of tight gas sandstone and carbonate rocks at the core scale. The following features were modeled via three-dimensional multiscale networks: microporosity within dissolved grains and pore-filling clay, cementation in the absence and presence of microporosity (each classified into uniform, pore-preferred, and throat-preferred modes), layering, vug, and microcrack inclusion. A priori knowledge of the extent and location of each process was assumed to be known. With the exception of an equal importance of macropores and pore-filling micropores, TBPs show little sensitivity to the fraction of micropores present. In general, significant sensitivity of the TBPs was observed for uniform and throat-preferred cementation. Layering parallel to the fluid flow direction had a considerable impact on TBPs whereas layering perpendicular to flow did not. Microcrack orientations seemed of minor importance in affecting TBPs.

58 GEOSCIENCES↗

DOE OSTI2016

Dependence of the rate of LiF ion pairing on the description of molecular interaction

We present an analysis of the dynamics of ion-pairing of Lithium Fluoride (LiF) in aqueous solvent using both detailed molecular simulation as well as reduced models within a Gener- alized Langevin Equation (GLE) framework. We explored the sensitivity of the ion-pairing phenomena to the details of descriptions of molecular interaction, comparing two empirical potentials to explicit quantum based density functional theory. We find quantitative differences in the potentials of mean force for ion-pairing as well as time dependent frictions that lead to variations in the rate constant and reactive flux correlation functions. These details reflect differences in solvent response to ion-pairing between different representations of molecular interaction and influence anharmonicity of the dynamic response. We find that the short time anharmonic response is recovered with a GLE parameterization. Recovery of the details of long time response may require extensions to the reduced model. We show that the utility of using a reduced model leads to a straight forward application of variational transition state the- ory concepts to the condensed phase system. The significance of this is reflected in the analysis of committor distributions and the variation of planar hypersurfaces, leading to an improved understanding of factors that determine the rate of LiF ion-pairing. CJM and GKS are supported by the U.S. Department of Energy‘s (DOE) Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest Na- tional Laboratory (PNNL) is operated for the Department of Energy by Battelle. MDB is grateful for the support of Laboratory Directed Research and Development funding under the auspices of PNNL’s Laboratory Initiative Materials Synthesis and Simulation across Scales (MS3). Additional computing resources were generously allocated by PNNL’s Institutional Computing program. EP acknowledges support from PNNL’s Alternate Sponsored Fellowship program and IMPRS Dres- den.Support to P.J. from the Czech Science Foundation (grant P208/12/G016) and the Academy of Sciences (Praemium Academie award) is gratefully acknowledged.

Pluharova, Eva↗

DOE OSTI2016

TU-H-206-01: An Automated Approach for Identifying Geometric Distortions in Gamma Cameras

Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; low values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deployed to monitor data from all gamma cameras within our health system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Integrated Data Analysis to Improve Te and Zeff Measurement on NSTX-U

The multi-energy soft x-ray (ME-SXR) camera is a new concept for a fusion plasma diagnostic based on a pixelated detector with adjustable photon cutoff thresholds. It is a single, compact, solid-state detector which combines spatial and spectral sensitivity and can be configured remotely at run time, making it ideal for a fusion environment. The detector can be configured for application to a wide range of physics problems including impurity transport, electron temperature profiles, and non-thermal effects. The detector has been installed on the Madison Symmetric Torus (MST) as part of a collaboration between Princeton Plasma Physics Laboratory and the University of Wisconsin–Madison to develop the technology and analysis techniques. A full diagnostic forward model of the ME-SXR diagnostic has been developed to enable analysis of the raw photon count data. This model incorporates the atomic physics underlying the plasma emission, diagnostic geometry, and detector response characteristics. Employing this model, a Bayesian analysis approach is used to extract electron temperature from the raw data. This analysis approach uses all available information, clearly quantifies prior information, and handles uncertainties transparently. The ME-SXR camera is being used to study the 3D (helical, stellarator-like) state that forms in the MST device. This helical core structure has improved energy confinement. The transition to the helical state is accompanied by a rise in electron temperature in the central part of the helical structure, but then this enhancement fluctuates. The characteristics of this fluctuation are being compared to a magneto-hydrodynamics model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Studies of e+e- Pair Photo-Production on Proton Target at 8 GeV in the GlueX Experiment

Lepton pair production has played an important role in both nuclear and particle physics, being among the earliest calculations utilizing QED, and seen most famously in the discovery of the J= at BNL. A technique is presented for measuring the linear polarization of GeV scale photon beams through the detection of e+e? pairs photo-produced in the target. This technique is applied to the analysis of GlueX data on proton target. Simulation predicts the analyzing power for pair production to be :5725 ? 0:0025 for the GlueX data, and the analysis of experimental data gives a linear polarization of approximately 35%, in good agreement with other measurements of beam polarization. The pair production technique is complementary to other electromagnetic and hadronic measurements of beam polarization, and is generally applicable in experiments that allow for forward angle electron and positron identi?fication and tracking. To facilitate this study, a neural net was trained for e=? separation to eliminate the pion background. Further, it is demonstrated that these e+e? pairs are sensitive to the proton charge form factor, which opens up the possibility for a new method to measure the proton RMS charge radius.

Schick, Andrew↗

DOE OSTI2019

Surrogate Model Selection for Design Space Approximation And Surrogatebased Optimization

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for sensitivity analysis, uncertainty propagation and surrogate based optimization. This work evaluates the performance of eight surrogate modeling techniques for design space approximation and surrogate based optimization applications over a set of generated datasets with known characteristics. With this work, we aim to provide general rules for selecting an appropriate surrogate model form solely based on the characteristics of the data being modeled. Furthermore, the computational experiments revealed that, in general, multivariate adaptive regression spline models (MARS) and single hidden layer feed forward neural networks (ANN) yielded the most accurate predictions over the design space while Random Forest (RF) models most reliably identified the locations of the optimums when used for surrogate-based optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Barriers for interfacial back-electron transfer: A comparison between TiO 2 and SnO 2 /TiO 2 core/shell structures

Temperature dependent kinetics for back-electron transfer (BET) from electrons in TiO 2 or SnO 2 /TiO 2 core/shell nanoparticles to oxidized donor-bridge-acceptor (D-B-A) sensitizers is reported over a 110° range. Two D-B-A sensitizers (CF 3 -p and CF 3 -x) were utilized that differed only by the nature of the bridging ligand: a xylyl spacer that largely insulated the two redox active centers and a phenyl bridge that promoted strong electronic coupling and an adiabatic electron transfer mechanism. An Arrhenius analysis revealed that the activation energies were significantly larger for the core/shell oxides, E a = 32 ± 4 kJ/mol, compared to TiO 2 alone, E a = 22 ± 6 kJ/mol. The barriers for BET on sensitized TiO 2 were within the same range as previous literature reports, while this study represents the first quantification for SnO 2 /TiO 2 core/shell materials. Two different models were proposed to rationalize the larger barrier for the core/shell materials: (1) a band edge offset model and (2) a low energy trap state model with recombination from the TiO 2 rutile polymorph shell. The latter model was preferred and is in better agreement with the experimental data. The kinetic analysis also afforded the forward and reverse rate constants for the intramolecular equilibrium. In accordance with theoretical predictions and previous research, the absolute value of the free energy change was smaller for the adiabatic equilibrium provided by the phenyl bridge, i.e., |ΔG o ad | <|ΔG o |.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Aeroacoustic Analysis using Dynamic Mode Decomposition of Unsteady Pressure-Sensitive Paint Measurements

In this paper, we present a new method for the diagnosis and analysis of aeroacoustic phenomena. Taking advantage of the well-known property that the measurements of the Unsteady Pressure-Sensitive Paint (uPSP) have much higher spatial resolution compared to those of the conventional pressure transducers, the method is based on the visualization and analysis of the outputs of the Dynamic Mode Decomposition (DMD) of the uPSP measurements. The uPSP measurements were collected with four Phantom high-speed cameras in the Ascent Transient Aerodynamics Test (ATAT) of the Space Launch System (SLS) Block 1 cargo vehicle in the 11-by-11-foot transonic test section of the Unitary Plan Wind Tunnel (UPWT) at NASA Ames Research Center in September 2019. The method presented in this paper is demonstrated by investigating an interesting phenomenon observed in the SLS ATAT – the vortex shedding tone generated by the forward attachment of the Solid Rocket Booster (SRB). As examples, the DMD outputs of the uPSP measurements in a subsonic test in the SLS ATAT are presented. It is shown the information retrieved from the DMD outputs of the uPSP measurements can be effectively used in the identification and diagnosis of the aeroacoustic phenomena. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. Funding was provided by the NASA Aerosciences Evaluation and Test Capabilities Portfolio Office.

acoustics↗

NASA NTRSDate not supplied

Aeroacoustic Analysis using Dynamic Mode Decomposition of Unsteady Pressure-Sensitive Paint Measurements

acoustics↗