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NASA NTRS1971

Statistical analysis of the modal properties of large structural systems.

A theory is developed to predict eigenvalue and eigenvector uncertainty in large dynamic models. The uncertainty is based on physical property uncertainty and should not be confused with numerical roundoff, although the method can be extended to include the latter. The theory, when implemented on a computer, is used to analyze the uncertainties in frequencies and mode shapes based on uncertainties in mass, stiffness, modulus of elasticity, etc. The method incorporates a linear statistical model which is quite adequate for handling property uncertainties of 10% or more. The model is not limited to small systems but uses certain statistical assumptions as well as selective matrix manipulations to keep the size of all matrix operations to within the number of degrees of freedom of the system. Examples are given for two longitudinal vibration problems, and the results are supported by a Monte Carlo analysis.

Collins, J. D.↗

DOE OSTI2022

Optimizing observables with machine learning for better unfolding

Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

235 U(n, f) Independent fission product yield and isomeric ratio calculated with the statistical Hauser–Feshbach theory

In this work, we have developed a Hauser–Feshbach fission fragment decay model, HF 3 D, which can be applied to the statistical decay of more than 500 primary fission fragment pairs (1,000 nuclides) produced by the neutron induced fission of 235 U. The HF 3 D model numerically integrates fission fragment yield and their initial excitation energy, spin and parity distributions for these 1,000 nuclides. The fission fragment yield Y ( A ) and the total kinetic energy are model inputs, and we estimate them from available experimental data for the 235 U(n th ,f) system. The model parameters in the statistical decay calculation are adjusted to reproduce some fission observables, such as the neutron emission multiplicity v - , its distribution P ( ν ), and the mass dependence v - ( A ). The calculated fission product yield (FPY) and isomeric ratio (IR) are compared with experimental data. The calculated independent FPY Y I ( A ) at the thermal energy reproduces the experimental data well, while the calculated IRs tend to be lower than the Madland–England model prediction. The HF 3 D model is extended to higher incident neutron energies up to the second-chance fission threshold. We demonstrate for the first time that most of the IRs stay constant, although the production of isomeric state changes as the incident energy increases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

Global invariants in ideal magnetohydrodynamic turbulence

Magnetohydrodynamic (MHD) turbulence is an important though incompletely understood factor affecting the dynamics of many astrophysical, geophysical, and technological plasmas. As an approximation, viscosity and resistivity may be ignored, and ideal MHD turbulence may be investigated by statistical methods. Incompressibility is also assumed and finite Fourier series are used to represent the turbulent velocity and magnetic field. The resulting model dynamical system consists of a set of independent Fourier coefficients that form a canonical ensemble described by a Gaussian probability density function (PDF). This PDF is similar in form to that of Boltzmann, except that its argument may contain not just the energy multiplied by an inverse temperature, but also two other invariant integrals, the cross helicity and magnetic helicity, each multiplied by its own inverse temperature. However, the cross and magnetic helicities, as usually defined, are not invariant in the presence of overall rotation or a mean magnetic field, respectively. Although the generalized form of the magnetic helicity is known, a generalized cross helicity may also be found, by adding terms that are linear in the mean magnetic field and angular rotation vectors, respectively. These general forms are invariant even in the presence of overall rotation and a mean magnetic field. We derive these general forms, explore their properties, examine how they extend the statistical theory of ideal MHD turbulence, and discuss how our results may be affected by dissipation and forcing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Neutron Electric Dipole Moment and Tensor Charges from Lattice QCD

Here, we present lattice QCD results on the neutron tensor charges including, for the first time, a simultaneous extrapolation in the lattice spacing, volume, and light quark masses to the physical point in the continuum limit. We find that the “disconnected” contribution is smaller than the statistical error in the “connected” contribution. Our estimates in the modified minimal subtraction scheme at 2 GeV, including all systematics, are 𝑔$^{𝑑−𝑢}_𝑇$ =1.020⁢(76), 𝑔$^𝑑_𝑇$ = 0.774⁢(66), 𝑔$^𝑢_𝑇$ = −0.233⁢(28), and 𝑔$^𝑠_𝑇$ = 0.008⁢(9). The flavor diagonal charges determine the size of the neutron electric dipole moment (EDM) induced by quark EDMs that are generated in many new scenarios of 𝐶⁢𝑃 violation beyond the standard model. We use our results to derive model-independent bounds on the EDMs of light quarks and update the EDM phenomenology in split supersymmetry with gaugino mass unification, finding a stringent upper bound of 𝑑 𝑛 < 4 × 1⁢0 −28 𝑒 cm for the neutron EDM in this scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Heavy Flavor and Jet Studies for the Future Electron-Ion Collider to Explore the Hadronization Process

Heavy flavor production at the future Electron-Ion Collider (EIC) will allow us to precisely determine the quark/gluon fragmentation processes in vacuum and the nuclear medium especially within the poorly constrained kinematic region. Heavy flavor hadron and jet reconstructions with the recent EIC detector design have been studied in simulation. Results of corresponding physics projections such as the flavor dependent hadron nuclear modification factor R_{eA} R e A in electron+nucleus collisions will be shown. The statistical precision obtained by these proposed heavy flavor measurements for the future EIC provides a strong discriminating power in separating different theoretical predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments

The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Gaussian Beta Ensembles at High Temperature: Eigenvalue Fluctuations and Bulk Statistics

We study the limiting behavior of Gaussian beta ensembles in the regime where βn=const as n→∞. The results are (1) Gaussian fluctuations for linear statistics of the eigenvalues, and (2) Poisson convergence of the bulk statistics. (2) is an alternative proof of the result by Benaych-Georges and Péché (J Stat Phys 161(3):633–656, 2015) with the explicit form of the intensity measure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Solitons and shocks in dense astrophysical magnetoplasmas with relativistic degenerate electrons and positrons

The linear and nonlinear properties of the ion-acoustic (IA) waves are investigated in a relativistically degenerate magnetoplasma, whose constituents are the electrons, positrons, and ions. The electrons and positrons are assumed to obey the Fermi-Dirac statistics, whereas the cold ions are taken to be inertial and magnetized. In linear analysis, various limiting cases are discussed both analytically and numerically. However, for nonlinear studies, the well-known reductive perturbation technique is employed to derive the Zakharov-Kuznetsov and Zakharov-Kuznetsov Burgers equations in the presence of relativistically degenerate electrons and positrons. Furthermore, with the use of hyperbolic tangent method, the equations are simplified to admit the soliton and shock wave solutions. Numerically, it is shown that the amplitude, width, and phase speed associated with the localized IA solitons and shocks are significantly influenced by the various intrinsic plasma parameters relevant to our model. The present analysis can be useful for understanding the collective processes in dense astrophysical environments like neutron stars, where the electrons and positrons are expected to be relativistic and degenerate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment

Reduction in safety margin can be expected as passive structures and components undergo degradation with time. Limitations in the traditional probabilistic risk assessment (PRA) methodology constrain its value as an effective tool to address the impact of aging effects on risk and for quantifying the impact of aging management strategies in maintaining safety margins. A methodology has been developed to address multiple aging mechanisms involving large numbers of components (with possibly statistically dependent failures) within the PRA framework in a computationally feasible manner when the sequencing of events is conditioned on the physical conditions predicted in a simulation environment, such as the New Generation System Code (NGSC) concept. Both epistemic and aleatory uncertainties can be accounted for within the same phenomenological framework and maintenance can be accounted for in a coherent fashion. The framework accommodates the prospective impacts of various intervention strategies such as testing, maintenance, and refurbishment. The methodology is illustrated with several examples.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2004

Exploring New Pathways in Precipitation Assimilation

Precipitation assimilation poses a special challenge in that the forward model for rain in a global forecast system is based on parameterized physics, which can have large systematic errors that must be rectified to use precipitation data effectively within a standard statistical analysis framework. We examine some key issues in precipitation assimilation and describe several exploratory studies in assimilating rainfall and latent heating information in NASA's global data assimilation systems using the forecast model as a weak constraint. We present results from two research activities. The first is the assimilation of surface rainfall data using a time-continuous variational assimilation based on a column model of the full moist physics. The second is the assimilation of convective and stratiform latent heating retrievals from microwave sensors using a variational technique with physical parameters in the moist physics schemes as a control variable. We will show the impact of assimilating these data on analyses and forecasts. Among the lessons learned are (1) that the time-continuous application of moisture/temperature tendency corrections to mitigate model deficiencies offers an effective strategy for assimilating precipitation information, and (2) that the model prognostic variables must be allowed to directly respond to an improved rain and latent heating field within an analysis cycle to reap the full benefit of assimilating precipitation information. of microwave radiances versus retrieval information in raining areas, and initial efforts in developing ensemble techniques such as Kalman filter/smoother for precipitation assimilation. Looking to the future, we discuss new research directions including the assimilation

Hou, Arthur↗

DOE OSTI2015

How random is a random vector?

Over 80 years ago Samuel Wilks proposed that the “generalized variance” of a random vector is the determinant of its covariance matrix. To date, the notion and use of the generalized variance is confined only to very specific niches in statistics. In this paper we establish that the “Wilks standard deviation” –the square root of the generalized variance–is indeed the standard deviation of a random vector. We further establish that the “uncorrelation index” –a derivative of the Wilks standard deviation–is a measure of the overall correlation between the components of a random vector. Both the Wilks standard deviation and the uncorrelation index are, respectively, special cases of two general notions that we introduce: “randomness measures” and “independence indices” of random vectors. In turn, these general notions give rise to “randomness diagrams”—tangible planar visualizations that answer the question: How random is a random vector? The notion of “independence indices” yields a novel measure of correlation for Lévy laws. In general, the concepts and results presented in this paper are applicable to any field of science and engineering with random-vectors empirical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Floor and ceiling effects in the EORTC QLQ-C30 Physical Functioning subscale among patients with advanced or metastatic breast cancer

The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 Physical Functioning subscale is a widely used patient-reported outcome measure that quantifies cancer patients' physical functioning. Strong floor/ceiling effects can affect a scale's sensitivity to change. The aim of this study was to characterize floor/ceiling effects of the physical functioning domain in patients with advanced/metastatic breast cancer enrolled in commercial clinical trials and a community-based trial. The clinical trial cohort comprised patients from 5 registrational trials submitted to the Food and Drug Administration for review (2010-2017). The community cohort comprised a subgroup of patients from the Alliance Patient Reported Outcomes to Enhance Cancer Treatment (PRO-TECT) trial. The distribution of patient responses to Physical Functioning items and the summed score were assessed at the baseline and 3-month follow-up for both cohorts. Descriptive statistics were used to determine floor/ceiling effects at the item and scale levels. The clinical trial cohort and the community cohort consisted of 2407 and 178 patients, respectively. Twenty-four percent or more of the respondents reported “not at all” for having trouble/needing help with each Physical Functioning item across both cohorts and measurement time points. Fourteen to twenty percent of the patients scored perfectly (100 of 100) on the Physical Functioning subscale summary measure (where higher scores indicated better physical functioning) across both cohorts and time points. Minor floor effects and notable ceiling effects were found at the item and scale levels of the Physical Functioning subscale, regardless of cohort, and this creates some uncertainty about its ability to detect changes in physical functioning among high-functioning patients. In conclusion, investigators may consider adding additional high-functioning items from the EORTC's item library to more accurately describe the impact of anticancer treatment on patients' physical functioning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Response theory of the ergodic many-body delocalized phase: Keldysh Finkel’stein sigma models and the 10-fold way

We derive the finite temperature Keldysh response theory for interacting fermions in the presence of quenched short-ranged disorder, as applicable to any of the 10 Altland–Zirnbauer classes in an Anderson delocalized phase with at least a U(1) continuous symmetry. In this formulation of the interacting Finkel’stein nonlinear sigma model, the statistics of one-body wave functions are encoded by the constrained matrix field, while physical correlations follow from the hydrodynamic density or spin response field, which decouples the interactions. Integrating out the matrix field first, we obtain weak (anti) localization and Altshuler–Aronov quantum conductance corrections from the hydrodynamic response function. This procedure automatically incorporates the correct infrared cutoff physics, and in particular gives the Altshuler–Aronov–Khmelnitsky (AAK) equations for dephasing of weak (anti)localization due to electron–electron collisions. We explicate the method by deriving known quantumcorrections in two dimensions for the symplectic metal class AII, as well as the spin-SU(2) invariant superconductor classes C and CI. We show that quantum conductance corrections due to the special modes at zero energy in nonstandard classes are automatically cut off by temperature, as previously expected, while the Wigner–Dyson class Cooperon modes that persist to all energies are cut by dephasing. We also show that for short-ranged interactions, the standard self-consistent solution for the dephasing rate is equivalent to a particular summation of diagrams via the self-consistent Born approximation. This should be compared to the corresponding AAK solution for long-ranged Coulomb interactions, which exploits the Markovian noise correlations induced by thermal fluctuations of the electromagnetic field. We discuss prospects for exploring the many-body localization transition as a dephasing catastrophe in short-range interacting models, as encountered by approaching from the ergodic side. - Highlights: • The Keldysh response theory for disordered, interacting fermions is derived. • The theory applies to any Altland–Zirnbauer class with at least a U(1) symmetry. • The correct infrared physics at finite temperature is automatically incorporated. • The Altshuler–Aronov–Khmelnitsky equations for dephasing are systematically derived. • A strategy to explore the MBL transition as a dephasing catastrophe is proposed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

TRISO fuel performance analysis: Uncertainty quantification toward optimization

Tri-structural isotropic (TRISO) fuel particles are a fuel form being considered for potential use in next-generation nuclear reactors (i.e., high-temperature gas-cooled reactors). Though the TRISO fuel manufacturing process has continually advanced in recent years, particle comparisons still reveal statistical variations and uncertainties in terms of geometric configurations and material properties. Given that the physical processes ongoing in TRISO fuel particles during reactor operation are highly correlated with each other, a small degree of uncertainty in one model may lead to significant uncertainty in another. This makes appropriate uncertainty quantification of TRISO fuel particles essential. However, one may wonder about the extent to which the current version of TRISO particles has been optimized, and whether any room remains for further improvements. This paper quantifies TRISO fuel performance model uncertainties that stem from geometric and material data. For this analysis, the BISON code was used, and the Advanced Gas Reactor (AGR)-2 experiment served as a reference case. A total of 10 5 calculations was performed for the uncertainty and optimization analysis, altering the geometric and material data within their uncertainty range. Lastly, the optimization potential of TRISO particles is evaluated from a fuel performance perspective.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings, and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius-Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely used convolutional network, U-Net, achieving averaged modified Kling-Gupta Efficiency of 0.88 and 0.94 on the test dataset for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling.

54 ENVIRONMENTAL SCIENCES↗