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DOE OSTI2023

Space-based quantum networking in the presence of a nuclear disturbed environment

Space-based quantum networks provide a means for near-term long-distance transmission of quantum information. Here this article analyzed the performance of a downlink quantum network between a low-Earth-orbit satellite and an observatory operating in less-than-ideal atmospheric conditions. The effects from fog, haze, and a nuclear disturbed environment on the long-range distribution of quantum states were investigated. A density matrix that estimates the quantum state by capturing the effects from increased signal loss and elevated background noise to estimate the state fidelity of the transmitted quantum state was developed. It was found that the nuclear disturbed environment and other atmospheric effects have a degrading effect on the quantum state. These environments impede the ability to perform quantum communications for the duration of the effects. In the case of the nuclear disturbed environment, the nuclear effects subside quickly, and network performance should return to normal by the next satellite pass.

42 ENGINEERING↗

DOE OSTI2020

Dynamic pulse propagation modelling for predictive femtosecond-laser-microbonding of transparent materials

A dynamic pulse propagation modeling for femtosecond laser bonding of Borofloat glass is presented. The temperature evolution and internal modifications are predicted by incorporating the nonlinear electron dynamics along with temperature dependent thermal properties. The modelling predicts the spatial and temporal distribution of absorption coefficient and plasma density that gives quantitative estimations of the heat affected zone and weld geometry. The impact of focusing condition on heat affected zone and weld geometry is investigated, which for the first time to our knowledge allows to numerically determine the desired relative position between the geometrical focus of a femtosecond-laser-pulse and the interface of the two substrates to be welded. The prediction of the modelling on the offset distance is applied to weld Borofloat glass plates having optical contact and can be applied to other dielectric solids.

Sahoo, Pankaj K. (ORCID:0000000209437319)↗

DOE OSTI2026

Flight Arrival Data for Each Airport

This dataset contains flight arrival data for each airport, which serves as the input for generating load profiles and estimating energy needs and fleet and charger requirements. ![image](distribution-flight-arrivals.png) Distribution of annual flight arrivals across different airport categories.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7\sigma$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Solid Oxide Based Electrolysis and Stack Technology with Ultra-High Electrolysis Current Density (>3A/cm 2 ) and Efficiency

Hydrogen, a valuable commodity gas, is increasingly recognized as an important fuel and energy storage pathway of the future. This project aims to develop an innovative solid oxide based electrolysis cell and stack technology with ultra-high steam electrolysis current (>3A/cm 2 ) for potentially ultra-low-cost, highly efficient hydrogen production from diverse renewable sources. In this project, solid oxide based high power density cells (HiPoD cells) were developed. Those cells demonstrated capability of operating at ultra-high electrolysis current density of 6 A/cm 2 at 1.67V. During a long term steady state electrolysis test, a HiPoD cell was operated for more than 1000 hours at 3 A/cm 2 with a low degradation rate of 1.8% per 1000 hours. A 20-cell electrolysis stack was built using HiPoD cells and an ultra-compact, low-cost stack design platform. This stack was used to explore the boundaries of high current density electrolysis operation and achieved an incredible stack current density of 3 A/cm 2 with an average cell voltage of only 1.493 V. The stack was further operated in steady-state electrolysis at 2 A/cm 2 for more than 1000 hours with an overall degradation of 0.57% per 1000 hours. With an improved understanding of electrolysis operation at ultra-high current density, a new compact stack architecture (CSA) design with scalability up to 55 kW (or 38 kg H 2 per day) was tested. A number of 45-cell CSA stack was built and demonstrated 10 kW and 245 g/hr hydrogen production at 1.8 A/cm 2 electrolysis current density. In addition to cell and stack development, a preliminary system design was developed by integrating the inputs from electrochemistry, cell/stack performance data, and system level implications of configuration and operational parameters. A module design was created based on a circular array of six stacks surrounded in a cool pressure vessel. Further architectural and controls strategies such as stack power control and instrumentation were determined through the development of the module. A comprehensive techno-economic study of an ultra-high current density SOEC system integrated with renewable energy sources has been conducted. To determine the estimated cost of hydrogen for the system, the H2A Current Distributed Hydrogen Production Model version 3.101 was applied. Annual system production cases at both commercial and industrial electricity rates as per the H2A model was analyzed. The option of meeting DOE 2020 Advanced Electrolysis Technologies targets were discussed. Feedstock electricity represents the bulk of projected hydrogen cost with either electricity rate. At an annual production volume of 250 units, H2A hydrogen cost of $2.3/kg can be achieved at electricity cost around 3.5¢/kWh.

08 HYDROGEN↗

DOE OSTI2017

Impact of wetland decline on decreasing dissolved organic carbon concentrations along the Mississippi River continuum

Prior to discharging to the ocean, large rivers constantly receive inputs of dissolved organic carbon (DOC) from tributaries or fringing floodplains and lose DOC via continuous in situ processing along distances that span thousands of kilometers. Current concepts predicting longitudinal changes in DOC mainly focus on in situ processing or exchange with fringing floodplain wetlands, while effects of heterogeneous watershed characteristics are generally ignored. We analyzed results from a 17-year time-series of DOC measurements made at seven sites and three expeditions along the entire Mississippi River main channel with DOC measurements made every 17 km. The results show a clear downstream decrease in DOC concentrations that was consistent throughout the entire study period. Downstream DOC decreases were primarily (~63–71%) a result of constant dilutions by low-DOC tributary water controlled by watershed wetland distribution, while in situ processing played a secondary role. We estimate that from 1780 to 1980 wetland loss due to land-use alterations caused a ca. 58% decrease in in DOC concentrations in the tributaries of the Mississippi River. DOC reductions caused by watershed wetland loss likely impacted the capacity for the river to effectively remove nitrogen via denitrification, which can further exacerbate coastal hypoxia. Lastly, these findings highlight the importance of watershed wetlands in regulating DOC longitudinally along the headland to ocean continuum of major rivers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Search for hyperluminous infrared dust-obscured galaxies selected with WISE and SDSS

We aim to search for hyperluminous infrared (IR) galaxies (HyLIRGs) with IR luminosity L{sub IR} > 10{sup 13} L{sub ☉} by applying the selection method of dust-obscured galaxies (DOGs). They are spatially rare but could correspond to a maximum phase of cosmic star formation (SF) and/or active galactic nucleus (AGN) activity; hence, they are a crucial population for understanding the SF and mass assembly history of galaxies. Combining the optical and IR catalogs obtained from the Sloan Digital Sky Survey (SDSS) and Wide-field Infrared Survey Explorer (WISE), we performed the extensive HyLIRGs survey; we selected 5311 IR-bright DOGs with i − [22] > 7.0 and flux at 22 μm > 3.8 mJy in 14,555 deg{sup 2}, where i and [22] are i-band and 22 μm AB magnitudes, respectively. Among them, 67 DOGs have reliable spectroscopic redshifts that enable us to estimate their total IR luminosity based on the spectral energy distribution fitting. Consequently, we successfully discovered 24 HyLIRGs among the 67 spectroscopically confirmed DOGs. We found that (i) i − [22] color of IR-bright DOGs correlates with the total IR luminosity and (ii) the surface number density of HyLIRGs is >0.17 deg{sup −2}. A large fraction (∼73%) of IR-bright DOGs with i − [22] > 7.5 show L{sub IR} > 10{sup 13} L{sub ☉}, and the DOG criterion we adopted could be independently effective against the “W1W2-dropout method,” based on four WISE bands, for searching hyperluminous IR populations of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

MALT90 Kinematic Distances to Dense Molecular Clumps

Using molecular-line data from the Millimetre Astronomy Legacy Team 90 GHz Survey (MALT90), we have estimated kinematic distances to 1905 molecular clumps identified in the ATLASGAL 870 μ m continuum survey over the longitude range 295° < l < 350°. The clump velocities were determined using a flux-weighted average of the velocities obtained from Gaussian fits to the HCO{sup +}, HNC, and N{sub 2}H{sup +} (1–0) transitions. The near/far kinematic distance ambiguity was addressed by searching for the presence or absence of absorption or self-absorption features in 21 cm atomic hydrogen spectra from the Southern Galactic Plane Survey. Our algorithm provides an estimation of the reliability of the ambiguity resolution. The Galactic distribution of the clumps indicates positions where the clumps are bunched together, and these locations probably trace the locations of spiral arms. Several clumps fall at the predicted location of the far side of the Scutum–Centaurus arm. Moreover, a number of clumps with positive radial velocities are unambiguously located on the far side of the Milky Way at galactocentric radii beyond the solar circle. The measurement of these kinematic distances, in combination with continuum or molecular-line data, now enables the determination of fundamental parameters such as mass, size, and luminosity for each clump.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest

We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

ldquants

1. Provide cleaned and filtered data stream from multiple ARM disdrometers; 2. Estimate commonly-used physical parameters of the Drop Size Distribution (DSD). 3. Calculate radar equivalent quantities such as the Radar Reflectivity Factor Z.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Retrieval of warm precipitation microphysics

Intrieri et al. (1993) and later O’Connor et al. (2005) proposed a technique to constrain water drop size distribution using lidar backscatter (related to water drop cross-section) and Doppler spectral width (related to the width of the water drop size distribution). This radar-lidar technique can be used to estimate precipitation rate and microphysical characteristics at all levels in the subcloud layer when collocated radar and ceilometer observations are available. We apply this technique to the vertically pointing ARM ceilometer lidar and Ka-band Zenith Radar (KAZR2) pair. The O’Connor et al. (2005) technique requires ceilometer backscatter to be calibrated and remapped to the radar spatio-temporal resolution (here 2 s x 30 m). Ceilometer backscatter is calibrated following a variation of the O'Connor et al. (2004) technique by scaling observed path-integrated backscatter in thick stratocumulus to match theoretical cloud lidar ratio values. Satisfactory conditions for ceilometer backscatter calibration are identified as the first (in time) 20-min periods each day with standard deviation of lidar ratio smaller than 1.5. The observed backscatter during the “satisfactory 20-min period” are input to Hogan (2006)’s multi scattered model to determine a daily backscatter calibration factor. For days where satisfactory conditions are not observed, a climatological calibration factor of 1.35 is used to calibrate the observed backscatter. Calibrated ceilometer backscatter is subsequently mapped on the KAZR2 time-height grid using a nearest-neighbor approach. Following Kollias et al. (2019), KAZR2 calibration is performed using collocated surface-based Parsivel laser disdrometer-equivalent radar reflectivity estimates during light precipitation events. This radar-lidar technique generates time-height maps of precipitation rate from 200 m above ground level to 90 m below cloud base height that are filtered for aerosol contamination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

vdisquants: Video Distrometer derived radar equivalent quantities. Retrievals from the VDIS instrument providing radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Numerical investigation of microbial quorum sensing under various flow conditions

Microorganisms efficiently coordinate phenotype expressions through a decision-making process known as quorum sensing (QS). We investigated QS amongst distinct, spatially distributed microbial aggregates under various flow conditions using a process-driven numerical model. Model simulations assess the conditions suitable for QS induction and quantify the importance of advective transport of signaling molecules. In addition, advection dilutes signaling molecules so that faster flow conditions require higher microbial densities, faster signal production rates, or higher sensitivities to signaling molecules to induce QS. However, autoinduction of signal production can substantially increase the transport distance of signaling molecules in both upstream and downstream directions. We present empirical approximations to the solutions of the advection–diffusion–reaction equation that describe the concentration profiles of signaling molecules for a wide range of flow and reaction rates. These empirical relationships, which predict the distribution of dissolved solutes along pore channels, allow to quantitatively estimate the effective communication distances amongst multiple microbial aggregates without further numerical simulations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Equipping Neural Network Surrogates with Uncertainty for Propagation in Physical Systems

Coarse-grained or filtered models typically rely on closure models to account for unresolved scales. For instance, large eddy simulation for modeling turbulent fluid flows explicitly resolves the largest scales, but requires modeling closure terms to account for the sub-filter scales. With the vast amount of data available from high-fidelity simulations, there are unique opportunities to leverage data-driven modeling techniques to formulate expressive and flexible closure models. Despite their flexibility, data-driven models struggle in domain shift settings, i.e. when deployed in configurations not captured in the training dataset. In particular, the efficacy of neural network surrogates is difficult to assess a priori due to the deterministic, point-estimate nature of predictions. In high-consequence applications, such models require reliable uncertainty estimates in the data-informed and out-of-distribution regimes. To quantify uncertainties in both regimes, we employ Bayesian neural networks which are able to capture both epistemic and aleatoric uncertainties. We will discuss challenges associated with the training and evaluation of these networks. Furthermore, we will discuss uncertainty embedding strategies to enable efficient sampling and propagation of uncertainty through high-fidelity simulations.

Bayesian neural networks↗

DOE OSTI2025

Likelihood-Based Particle Identification in the Short-Baseline Near Detector

Accurate particle identification is crucial in any high-energy physics experiment, allowing scientists to understand the unique interactions and mechanisms at play in a detector. In this project, I develop and study a new particle identification (PID) algorithm for the Short-Baseline Near Detector, a likelihood-based approach, different from out current $\chi^2$ method. A likelihood estimation offers a more physically motivated strategy for PID. The distribution random energy losses of charged particles traveling through a medium are described by the Vavilov probability density function. By using this model, we can account for random energy losses and construct likelihood functions specific to each particle type, potentially enabling a more accurate method for PID.

Vanderwaal, Sophia [U. Alabama, Huntsville] (ORCID↗

DOE OSTI2019

Interparticle Interactions in Dilute Solutions of Polyelectrolyte Complex Micelles.

The application of dilute solutions of poly electrolyte complex (PEC) micelles for delivering therapeutic nucleic acids into disease sites has gained momentum. This Letter reports a detailed characterization of PEC micelles in dilute solutions including their internal structures and the determination of the interparticle interactions. The polymer concentration ranges from 0.1 to 0.5 wt %, a regime where micelle-micelle interactions are infrequent. We employ synchrotron small-angle X-ray scattering (SAXS) to simultaneously probe the morphology, internal structure, and radius of gyration (R-g) of the self-assemblies formed by charged diblock polyelectrolytes and homopolyelectrolytes. The emerging appearance of the structure factor in SAXS profiles with the increasing polymer concentration demonstrates the presence of the repulsive intermicellar correlations, which is further confirmed by the differences between the "reciprocal R-g" estimated by Guinier approximation and the "real space R-g" determined by pair distribution functions. We find that the soft corona chains tethered on the surface of phase-separated complex domains are compressed when micelles come close to the point where a hard-sphere interaction takes over. These findings contribute to the fundamental understanding of the structure and space-filling constraints in the complexation-driven self-assemblies and advance the rational design of cationic polymer-based nonviral gene delivery vectors.

Wu, Hao↗

DOE OSTI2020

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) [SWR-20-115]

GET-Solar (Generation forecasting and parameter Estimation Tool for Solar) can be used to estimate behind the meter (BTM) solar generation from limited datasets. Using distributed sets of diverse solar sites, but with close spatial proximity, and/or limited historical PV generation information can be used to generate of solar profiles. This is useful for applications where partial solar generation data is available, and on-site irradiance data is not available. GET-Solar calculates PV technical data parameters and calculates output by modeling shading and cloud cover impacts. Clear sky irradiance profiles are used to calculate base generation profiles on which the local shading and cloud cover layers are added. In order to arrive at clear sky generation data, PV mounting information like panel tilt and and angle is needed. For cases where PV mounting information is missing, panel tilt and azimuth angle can be estimated using a non-linear optimization algorithm. GET-Solar can also be used for forecasting applications where historical PV data is available to estimate local shading at various solar positions. This overall framework can be used for estimation of solar parameters, and assisting in analyzing and modeling diverse sets of DER where solar generation is also present in utility meter data. GET-Solar has been developed in Python and uses the Pyomo optimization language.

Abraham, Sherin Ann↗

DOE OSTI2020

"Sintering" Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

3D printing, Additive Manufacturing, polymer, sint↗