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DOE OSTI2018

Dissipative tunnelling by means of scaled trajectories

Dissipative quantum tunnelling through an inverted parabolic barrier is considered in the presence of an electric field. A Schrödinger–Langevin or Kostin quantum–classical transition wave equation is used and applied resulting in a scaled differential equation of motion. A Gaussian wave packet solution to the resulting scaled Kostin nonlinear equation is assumed and compared to the same solution for the scaled linear Caldirola–Kanai equation. The resulting scaled trajectories are obtained at different dynamical regimes and friction cases, showing the gradual decoherence process in this open dynamics. Theoretical results show that the transmission probabilities are always higher in the Kostin approach than in the Caldirola–Kanai approach in the presence or not of an external electric field. This discrepancy should be understood due to the presence of an environment since the corresponding open dynamics should be governed by nonlinear quantum equations, whereas the second approach is issued from an effective Hamiltonian within a linear theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Search for electroweak-scale dijet resonances using trigger-level analysis with the ATLAS detector in 132 fb −1 of 𝑝⁢𝑝 collisions at $\sqrt{𝑠}$ = 13 TeV

This article reports on a search for dijet resonances using 132 fb −1 of 𝑝⁢𝑝 collision data recorded at $\sqrt{𝑠}$ = 13 TeV by the ATLAS detector at the Large Hadron Collider. The search is performed solely on jets reconstructed within the ATLAS trigger to overcome bandwidth limitations imposed on conventional single-jet triggers, which would otherwise reject data from decays of sub-TeV dijet resonances. Collision events with two jets satisfying transverse momentum thresholds of 𝑝 T ≥ 85 GeV and jet rapidity separation of |𝑦*| <0.6 are analysed for dijet resonances with invariant masses from 375 to 1800 GeV. A data-driven background estimate is used to model the dijet mass distribution from multijet processes. No significant excess above the expected background is observed. Upper limits are set at 95% confidence level on coupling values for a benchmark leptophobic axial-vector 𝑍′ model and on the production cross section for a new resonance contributing a Gaussian-distributed line-shape to the dijet mass distribution.

hadron colliders↗

DOE OSTI2017

Methods for improving the damage performance of fused silica polished by magnetorheological finishing

The laser-induced damage threshold of fused-silica samples processed via magnetorheological finishing is investigated for polishing compounds depending on the type of abrasive material and the post-polishing surface roughness. The effectiveness of laser conditioning is examined using a ramped pre-exposure with the same 351-nm, 3-ns Gaussian pulses. Lastly, we examine chemical etching of the surface and correlate the resulting damage threshold to the etching protocol. A combination of etching and laser conditioning is found to improve the damage threshold by a factor of ~3, while maintaining <1-nm surface roughness.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The Poisson tensor completion non-parametric differential entropy estimator

We introduce the Poisson tensor completion (PTC) estimator, a non-parametric differential entropy estimator. The PTC estimator leverages inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial Poisson process. The Poisson tensor decomposition leads to a completion of the intensity measure over all bins—including those containing few to no samples—and leads to our proposed PTC differential entropy estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values and so can be safely used directly in entropy estimation. Our estimator is the first tensor-based estimator that exploits the underlying spatial Poisson process related to the histogram explicitly when estimating the probability density with low-rank tensor decompositions for the purpose of tensor completion. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

42 ENGINEERING↗

DOE OSTI2017

Cosmic Rays in Intermittent Magnetic Fields

The propagation of cosmic rays in turbulent magnetic fields is a diffusive process driven by the scattering of the charged particles by random magnetic fluctuations. Such fields are usually highly intermittent, consisting of intense magnetic filaments and ribbons surrounded by weaker, unstructured fluctuations. Studies of cosmic-ray propagation have largely overlooked intermittency, instead adopting Gaussian random magnetic fields. Using test particle simulations, we calculate cosmic-ray diffusivity in intermittent, dynamo-generated magnetic fields. The results are compared with those obtained from non-intermittent magnetic fields having identical power spectra. The presence of magnetic intermittency significantly enhances cosmic-ray diffusion over a wide range of particle energies. We demonstrate that the results can be interpreted in terms of a correlated random walk.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1977

A simulation of air pollution model parameter estimation using data from a ground-based LIDAR remote sensor

One way to obtain estimates of the unknown parameters in a pollution dispersion model is to compare the model predictions with remotely sensed air quality data. A ground-based LIDAR sensor provides relative pollution concentration measurements as a function of space and time. The measured sensor data are compared with the dispersion model output through a numerical estimation procedure to yield parameter estimates which best fit the data. This overall process is tested in a computer simulation to study the effects of various measurement strategies. Such a simulation is useful prior to a field measurement exercise to maximize the information content in the collected data. Parametric studies of simulated data matched to a Gaussian plume dispersion model indicate the trade offs available between estimation accuracy and data acquisition strategy.

Kibler, J. F.↗

NASA NTRS1976

Modeling laser velocimeter signals as triply stochastic Poisson processes

Previous models of laser Doppler velocimeter (LDV) systems have not adequately described dual-scatter signals in a manner useful for analysis and simulation of low-level photon-limited signals. At low photon rates, an LDV signal at the output of a photomultiplier tube is a compound nonhomogeneous filtered Poisson process, whose intensity function is another (slower) Poisson process with the nonstationary rate and frequency parameters controlled by a random flow (slowest) process. In the present paper, generalized Poisson shot noise models are developed for low-level LDV signals. Theoretical results useful in detection error analysis and simulation are presented, along with measurements of burst amplitude statistics. Computer generated simulations illustrate the difference between Gaussian and Poisson models of low-level signals.

Mayo, W. T., Jr.↗

NASA NTRS1987

Photon counting image sensor development for astronomical applications

Specially built intensified CCD (ICCD) detector tubes were purchased and the performance of the electron bombardment process was investigated. In addition to studying the signal characteristics of the photoevents, there was interest in demonstrating that back-illuminated chips were not susceptible to radiation damage to their clocking electrodes. How to perform a centroid analysis for a 2-dimensional Gaussian distribution of charge is described. Measurement of the projection (along columns or rows) of the average charge spread profile is discussed. The development and flight of the Interstellar Medium Absorption Profile Spectrograph (IMAPS) is discussed.

Jenkins, Edward B.↗

NASA NTRS1991

Structural behavior of scientific balloons - Finite element simulation and verification

An off-the-shelf nonlinear finite element code was used to analyze fully inflated scientific balloons. The thin balloon film was modeled by shell bending elements. Numerical difficulties caused by insignificant bending stiffness terms were overcome by introducing some artificial bending stiffness. This approximation is justified by the fact that in thin shells with nonzero Gaussian curvature the membrane solution component is essentially independent of the bending solution component. Perturbation of the coveraged solution by increasing the bending stiffness by a full decade verified this assertion. This analytical approach was experimentally verified. As a result of this verification process it was discovered that the generally accepted linearly visco-elastic model for polyethylene film is inappropriate for a significant planar (as opposed to uniaxial) stress state. A linear elastic model presents a good approximation for planar stress states.

Schur, Willi W.↗

DOE OSTI2025

Gaussian integral method for void fraction

Here, a novel method, the Gaussian Integral Method (GIM), is presented for calculating void fractions in Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) simulations. GIM is versatile and applicable to various grid types, including structured and unstructured polyhedral meshes, without requiring special boundary treatments. An optimization technique is introduced to make GIM independent of grid resolution and type. The method is validated against experimental data from a fluidized bed, demonstrating that GIM produces realistic simulations closely resembling experimental observations. Additionally, unstructured polyhedral grids using GIM outperform structured grids of equivalent resolution, yielding results more aligned with experimental data. The gradient of the void fraction is computed in the CFD solver and utilized in the DEM solver for precise estimation at particle locations. Overall, GIM provides an effective solution for void fraction calculations in particulate media simulations with complex geometries, enhancing the accuracy and applicability of CFD-DEM simulations for industrial processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

rascalc : a jackknife approach to estimating single- and multitracer galaxy covariance matrices

ABSTRACT To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer data sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2015

Physical Models of Layered Polar Firn Brightness Temperatures from 0.5 to 2 GHz

We investigate physical effects influencing 0.5-2 GHz brightness temperatures of layered polar firn to support the Ultra Wide Band Software Defined Radiometer (UWBRAD) experiment to be conducted in Greenland and in Antarctica. We find that because ice particle grain sizes are very small compared to the 0.5-2 GHz wavelengths, volume scattering effects are small. Variations in firn density over cm- to m-length scales, however, cause significant effects. Both incoherent and coherent models are used to examine these effects. Incoherent models include a 'cloud model' that neglects any reflections internal to the ice sheet, and the DMRT-ML and MEMLS radiative transfer codes that are publicly available. The coherent model is based on the layered medium implementation of the fluctuation dissipation theorem for thermal microwave radiation from a medium having a nonuniform temperature. Density profiles are modeled using a stochastic approach, and model predictions are averaged over a large number of realizations to take into account an averaging over the radiometer footprint. Density profiles are described by combining a smooth average density profile with a spatially correlated random process to model density fluctuations. It is shown that coherent model results after ensemble averaging depend on the correlation lengths of the vertical density fluctuations. If the correlation length is moderate or long compared with the wavelength (approximately 0.6x longer or greater for Gaussian correlation function without regard for layer thinning due to compaction), coherent and incoherent model results are similar (within approximately 1 K). However, when the correlation length is short compared to the wavelength, coherent model results are significantly different from the incoherent model by several tens of kelvins. For a 10-cm correlation length, the differences are significant between 0.5 and 1.1 GHz, and less for 1.1-2 GHz. Model results are shown to be able to match the v-pol SMOS data closely and predict the h-pol data for small observation angles.

layered polar firn emission↗

DOE OSTI2015

Multivariate moment closure techniques for stochastic kinetic models

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Full-scale composting of sewage sludge and market waste: Stability monitoring and odor dispersion modeling

Highlights: • Composting process of different organic waste was monitored at full-scale. • Relationships among odor emissions and analytical and biological variables were found. • Removal of nitrogen and volatile solids was the main responsible for odor generation. • Biomethanization pretreatment reduced odor impact at the beginning of composting. • The highest odor immission occurred in summer season for a wind speed of 2.6 m/s. The aim of this study was to assess the odor immission derived from full-scale composting of different abundant and highly pollutant organic waste: sewage sludge with bulking agent (SL), sewage sludge pretreated through anaerobic digestion and supplemented with bulking agent (SL-AD), and market waste with olive leaves (MW-OL). The combination of dynamic olfactometry and Gaussian dispersion modeling allowed both the quantification of odor emissions from each waste and the evaluation of their global odorous impact in nearby urban areas. Wind speed, summer and winter seasons, and atmospheric conditions were considered in the dispersion model. The results revealed that high wind speed (2.6 m/s) increases the global odor immission in summer season, independently of atmospheric stability. However, the maximum odor immission concentration recommended for composting process was not exceeded in any case, which depends on each country/region. The experimental results also enable to evaluate the influence of several physico-chemical variables on odor emissions derived from composting. The removal of nitrogen and volatile solids was the main cause for odor generation. Moreover, the microbiological activity of each substrate was monitored throughout the process and different percentages of biodegradability were quantified depending on the type of substrate and pretreatment applied.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

2D stochastic-integral models for characterizing random grain noise in titanium alloys

We extend our previous work, in which we applied high-dimensional model representation (HDMR) and analysis of variance (ANOVA) concepts to the characterization of a metallic surface that has undergone a shot-peening treatment to reduce residual stresses, and has, therefore, become a random conductivity field. That example was treated as a onedimensional problem, because those were the only data available. In this study, we develop a more rigorous two-dimensional model for characterizing random, anisotropic grain noise in titanium alloys. Such a model is necessary if we are to accurately capture the 'clumping' of crystallites into long chains that appear during the processing of the metal into a finished product. The mathematical model starts with an application of the Karhunen-Loève (K-L) expansion for the random Euler angles, θ and φ, that characterize the orientation of each crystallite in the sample. The random orientation of each crystallite then defines the stochastic nature of the electrical conductivity tensor of the metal. We study two possible covariances, Gaussian and double-exponential, which are the kernel of the K-L integral equation, and find that the double-exponential appears to satisfy measurements more closely of the two. Results based on data from a Ti-7Al sample will be given, and further applications of HDMR and ANOVA will be discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Solubilization of PET in binary mixtures of HFIP and DCM

The dissolution of polyethylene terephthalate (PET) is a critical step for a solvent-based process, yet it typically requires highly corrosive or toxic solvents. Here, we investigate the solubilization and conformational behavior of PET in binary mixtures of hexafluoro-2-propanol (HFIP) and dichloromethane (DCM) as a strategy to reduce HFIP usage while maintaining effective dissolution. Small-angle neutron scattering (SANS) measurements reveal that PET remains molecularly dissolved in HFIP/DCM mixtures up to 50 vol% DCM. Analysis of PET chain conformations shows a transition from Gaussian behavior at low HFIP fractions to more swollen chains at intermediate compositions, accompanied by a counter-intuitive minimum in the radius of gyration at 50% HFIP. Complementary SANS measurements of the binary solvents demonstrate that compositional heterogeneity is maximized at this same solvent composition, suggesting a direct coupling between solvent microstructure and polymer dimensions. Molecular dynamics simulations corroborate the experimental findings, revealing solvent domain formation, preferential solvation of PET by HFIP, and a “caging” effect arising from solvent heterogeneity that leads to polymer coil compaction. Together, these results provide molecular-level insight into polymer behavior in mixed solvent systems and establish HFIP/DCM mixtures as a promising, more sustainable solvent platform for the PET post-process.

Arifuzzaman, Arif [ORNL]↗

NASA NTRS2010

Phase-Conjugate Receiver for Gaussian-State Quantum Illumination

An active optical sensor probes a region of free space that is engulfed in bright thermal noise to determine the presence (or absence) of a weakly reflecting target. The returned light (which is just thermal noise if no target is present, and thermal noise plus a weak reflection of the probe beam if a target is present) is measured and processed by a receiver and a decision is made on whether a target is present. It has been shown that generating an entangled pair of photons (which is a highly nonclassical state of light), using one photon as the probe beam and storing the other photon for comparison to the returned light, has superior performance to the traditional classical-light (coherent-state) target detection sensors. An entangled-photon transmitter and optimal receiver combination can yield up to a factor of 4 (i.e., 6 dB) gain in the error-probability exponent over a coherent state transmitter and optimal receiver combination, in a highly lossy and noisy scenario (when both sensors have the same number of transmitted photons). However, the receiver that achieves this advantage is not known. One structured receiver can close half of the 6-dB gap (i.e., a 3-dB improvement). It is based on phase-conjugating the returned light, then performing dual-balanced difference detection with the stored half of the entangled-photon pair. Active optical sensors are of tremendous value to NASA s missions. Although this work focuses on target detection, it can be extended to imaging (2D, 3D, hyperspectral, etc.) scenarios as well, where the image quality can be better than that offered by traditional active sensors. Although the current work is theoretical, NASA s future missions could benefit significantly from developing and demonstrating this capability. This is an optical receiver design whose components are, in principle, all implementable. However, the work is currently entirely theoretical. It is necessary to: 1. Demonstrate a bench-top proof of the theoretical principle, 2. Create an operational prototype off-the-bench, and 3. Build a practical sensor that can fly in a mission.

Erkmen, Baris I.↗

DOE OSTI2025

Real-time tracking and analysis of gas bubble dynamics in laser powder bed fusion using in-situ X-ray characterization and machine learning

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. Here, this study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

In-situ X-ray imaging↗