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DOE OSTI2015

A coordinated X-ray and optical campaign of the nearest massive eclipsing binary, δ Orionis Aa. II. X-ray variability

We present time-resolved and phase-resolved variability studies of an extensive X-ray high-resolution spectral data set of the δ Ori Aa binary system. The four observations, obtained with Chandra ACIS HETGS, have a total exposure time of ≈479 ks and provide nearly complete binary phase coverage. Variability of the total X-ray flux in the range of 5–25 Å is confirmed, with a maximum amplitude of about ±15% within a single ≈125 ks observation. Periods of 4.76 and 2.04 days are found in the total X-ray flux, as well as an apparent overall increase in the flux level throughout the nine-day observational campaign. Using 40 ks contiguous spectra derived from the original observations, we investigate the variability of emission line parameters and ratios. Several emission lines are shown to be variable, including S xv, Si xiii, and Ne ix. For the first time, variations of the X-ray emission line widths as a function of the binary phase are found in a binary system, with the smallest widths at ϕ = 0.0 when the secondary δ Ori Aa2 is at the inferior conjunction. Using 3D hydrodynamic modeling of the interacting winds, we relate the emission line width variability to the presence of a wind cavity created by a wind–wind collision, which is effectively void of embedded wind shocks and is carved out of the X-ray-producing primary wind, thus producing phase-locked X-ray variability.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature

Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.

14 SOLAR ENERGY↗

DOE OSTI2020

Origin and mitigation of wind noise on balloon-borne infrasound microbarometers

High-altitude monitoring of low-frequency acoustic waves (infrasound) on Earth has regained prominence in recent years, primarily driven by improvements in light-weight sensor technology and advances in scientific ballooning techniques. Balloon-borne infrasound monitoring is also being proposed as a remote sensing technique for planetary exploration. Contrary to ground-based infrasound monitoring, the infrasound noise background in the stratosphere as measured by a balloon remains uncharacterized and the efficacy of wind noise mitigation filters has not been investigated. In this study, an analysis of pressure data collected using infrasound microbarometers during the flight of a long-duration zero pressure balloon is presented. Here, a dramatic reduction of background noise in the stratosphere is demonstrated and it is shown that wind noise mitigation filters are not effective at reducing wind noise under these conditions. Results from this study demonstrate stratospheric balloons as a low-noise platform for infrasound monitoring and motivate the development of improved noise mitigation tools.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Comparison and validation of acoustic response models for wind noise reduction pipe arrays

The detection capability of the infrasound component of the International Monitoring System (IMS) is tightly linked to the performance of its wind noise reduction systems. The wind noise reduction solution implemented at all IMS infrasound measurement systems consists of a spatial distribution of air inlets connected to the infrasound sensor through a network of pipes. This system, usually referred to as “pipe array,” has proven its efficiency in operational conditions. The objective of this paper is to present the results of the comparison and validation of three distinct acoustic response models for pipe arrays. The characteristics of the models and the results obtained for a defined set of pipe array configurations are described. A field experiment using a newly developed infrasound generator, dedicated to the validation of these models, is then presented. The comparison between the modeled and empirical acoustic responses shows that two of the three models can be confidently used to estimate pipe array acoustic responses. Lastly, this study paves the way to the deconvolution of IMS infrasound data from pipe array responses and to the optimization of pipe array design to IMS applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

pyTCR: A tropical cyclone rainfall model for python

pyTCR is a climatology software package developed in the Python programming language. It integrates the capabilities of several legacy physical models and increases computational efficiency to allow rapid estimation of tropical cyclone (TC) rainfall consistent with the large-scale environment. Specifically, pyTCR implements a horizontally distributed and vertically integrated model [Zhu et al., 2013] for simulating rainfall driven by TCs. Along storm tracks, rainfall is estimated by computing the cross-boundary-layer, upward water vapor transport caused by different mechanisms including frictional convergence, vortex stretching, large-scale baroclinic effect (i.e., wind shear), topographic forcing, and radiative cooling [Lu et al., 2018]. The package provides essential functionalities for modeling and interpreting spatio-temporal TC rainfall data. pyTCR requires a limited number of model input parameters, making it a convenient and useful tool for analyzing rainfall mechanisms driven by TCs. To sample rare (most intense) rainfall events that are often of great societal interest, pyTCR adapts and leverages outputs from a statistical-dynamical TC downscaling model [Lin et al., 2023] capable of rapidly generating a large number of synthetic TCs given a certain climate. As a result, pyTCR significantly reduces computational effort and improves the efficiency in capturing extreme TC rainfall events at the tail of the distributions from limited datasets. Furthermore, the TC downscaling model is forced entirely by large-scale environmental conditions from reanalysis data or coupled General Circulation Models (GCMs), simplifying the projection of TC-induced rainfall and wind speed under future climate using pyTCR. Finally, pyTCR can be coupled with hydrological and wind models to assess risks associated with independent and compound events (e.g., storm surges and freshwater flooding).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Air Dispersion Modeling of Radioactive Releases During Proposed 236Z Rubble Pile Removal Activities

This report is part of the planning process for the removal of the 236Z rubble remaining as of January 1, 2018, at the Plutonium Finishing Plant (PFP) on the Hanford Site. This report documents an analysis considering standard techniques for the removal of demolition rubble. It does not document any decisions about the removal approaches. This document is intended to guide the establishment of a boundary for the application of an air monitoring regime. Atmospheric dispersion modeling using estimated release rates has been conducted to provide information on the location and levels of radioactive contamination that may be expected as the result of rubble removal activities. Hourly meteorological data collected over a 6-year period (2004–2009) were used to examine the effects of wind speed, direction, and stability on projected concentrations of contaminants in air and deposited on nearby surfaces. The source terms modeled in this report were based on the residual transuranic contamination levels that are anticipated to remain in the 236Z rubble following building demolition. The potential airborne concentrations were established using the five-factor formula from DOE-HDBK-3010 (DOE 1994) considering material-at-risk, damage ratio, airborne release fraction, respirable fraction, and leak path factor. Radioactive contamination emissions were calculated by release mechanism and rubble pile area for on-shift and off-shift activities. The emissions from the applicable sources were combined to provide emissions estimates for each day from each cleanup area. The U.S. Environmental Protection Agency’s (EPA’s) AERMOD computer code was used to estimate atmospheric dispersion and deposition of the released radioactive materials in the immediate vicinity of the planned rubble removal activities Both airborne and surface concentrations were modeled with AERMOD. Hourly derived air concentrations (DAC) were modeled for an array of receptors covering the rubble pile site and surrounding area. Peak (95th and 99th percentile) values of time-integrated air concentrations at these receptor points are derived from these hourly values, with modeling results reported as total incremental air concentrations in DAC-hours occurring over the selected time period. Total accumulated deposition amounts were also evaluated with AERMOD using the same array of receptors, with results reported as disintegrations per minute (dpm) per 100 cm2. Each rubble pile was considered in terms of its size, location, and estimated contamination levels. The modeling was based on the assumed sequence of the rubble removal activities. The results in this report are based on cleanup schedules and methods proposed by CHPRC. As of January 1, 2018, there are broadly three large rubble piles (west, footprint, and east) remaining of the 236Z Building process cell. It is assumed that the overall cleanup will require either 1) 30 days over a 6-week period, or 2) 60 days over a 12-week period. Work was assumed to be performed during 10-hour day shifts. The analysis shows that some releases of radioactive material are to be anticipated during the cleanup of the 236Z rubble piles. The modeling results presented here are closely tied to the details of how the rubble removal is to be conducted. The results indicate that for the rubble removal, the radiological exposures from the planned activities are predicted to be well below the designated limits for air and soil exposures. The cleanup of the 236Z main process cell rubble is expected to release some alpha-emitting radionuclides. However, results based on 6 years of hourly climatological data, two representative cleanup periods, realistic mitigating actions, and a bounding source term indicate that concentrations at the fence line of the PFP facilities should remain well below 12 DAC-hours/week. The results presented in this report are a reevaluation of a subset of the results presented in PNNL-20173 Rev. 4.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Validation of the AltRAM Physics Models for use with Compressed Natural Gas

The Alternative Fuels Risk Assessment Models (A1tRAM) toolkit combines Quantitative Risk Assessment (QRA) with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for methane which is being used as a surrogate for natural gas. For the unignited dispersion model, seven previously published experiments from credible sources were used to validate. The validation looked at gas concentrations with respect to the distance from the release point. Four of these were underexpanded jets (i.e. release velocity equal to or greater than local speed of sound) and the other three subsonic releases. The methane plume model in AltRAM matched both varieties well, with higher accuracy for the underexpanded releases. For the jet flame model, we compared the heat flux and thermal radiation data reported from five separate turbulent jet flame experiments to the quantities calculated by A1tRAM. Four of the five datasets were for underexpanded diffusion jets flames. While the results still match well enough to give a good estimate of what is occurring, the error is higher than what was seen with the plume model. For the underexpanded flames A1tRAM provided reasonable approximations, which would lead to conservative risk assessments. Some modeling errors can be attributed to environmental effects (i.e. wind) since most large scale flame experiments are conducted outdoors. A1tRAM has been shown to be a reasonably accurate tool for calculating the concentration or flame properties of natural gas releases. Improvements could still be made for the plume of subsonic releases and radiative heat fluxes to reduce the conservative nature of these predictions. These models can provide valuable information for the risk assessment of natural gas infrastructure.

03 NATURAL GAS↗

DOE OSTI2021

Validation of the HyRAM+ physics models for use with propane

The Hydrogen Risk Assessment Model Plus (HyRAM+) toolkit combines quantitative risk assessment with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. HyRAM+ is differentiated from HyRAM in that it includes models and leak data for other alternate fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for propane which is being used as a surrogate for autogas, which is a mixture of propane and butane and used in internal combustion engines in vehicles. For flame length comparisons, five previously published experiments from peer reviewed journals were used to validate our models. The validation looked at flame lengths and flame widths with respect to different leak diameters, mass flow rates, and source pressures. Most of the sources included more than one set of experimental data, which were collected using different methods (CCD cameras, IR visualization etc.). In general, HyRAM+ overpredicts the flame lengths by around 65%. For heat and radiation models, we compared the heat flux and radiation data reported from two different sources to the values calculated by HyRAM+. For higher mass flow rates, the HyRAM+ calculated flame length results gave a better estimate of what is found in the experiments (65% error), but a higher error (85%) is observed between the HyRAM+ calculated lengths and the experimental flame lengthsfor lower mass flows. Some differences can be attributed to outdoor environmental effects (i.e. wind speed) and uncertainties in jet flame shapes. The propane flame trajectory is predicted for a high Reynolds number case with Re = 12,500 and a low Reynolds number case where Re = 2,000. The Re=12,500 case which is momentum dominated matches well with the experimental flame trajectory, but the agreement for the bouancy driven low Reynolds number case is not as good. Dispersion modeling for unignited propane was also analyzed. We compared the mole fraction, mixture fraction, mean velocity, concentration half width, and inverse mass concentration over an axial distance from different credible journals to the values calculated by HyRAM+. The results display good agreement but generally, HyRAM+ predicts a wider profile for mole fraction and mixture fraction experiments. Overall, HyRAM+’s results are reasonable for predicting the flame length, heat flux, flame trajectory, and dispersion for propane and can be used in risk analyses

08 HYDROGEN↗

DOE OSTI2021

Lightning Effects on Pultruded Carbon Fiber Wind Blades.

Abstract not provided.

Williams, Michelle↗

DOE OSTI2022

Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes

As a leading mode of sea surface temperature (SST) variability over the North Atlantic in both observations and model simulations, the Atlantic Multidecadal Oscillation (AMO) can have a substantial influence on regional and global climate. By using Low-Frequency Component Analysis, we explore the uncertainties of the resulting AMO indices and the corresponding spatial patterns derived from three observational SST datasets. We found that the known coherent spatial pattern of the AMO at the basin scale over the North Atlantic appears in two out of the three datasets. Further analysis indicates that both the warming trend and the different techniques used to construct these observed gridded SSTs contribute to the AMO’s spatial coherence over the North Atlantic, especially during periods of sparse data sampling. The SST in the Extended Reconstructed SST dataset version 5 (ERSSTv5), changes from being systematically below the other datasets during the dense sampling periods on either side of the Second World War (WWII), to systematically above the other datasets during WWII, thereby introducing an artificial 10–20-year variability that affects the AMO’s spatial coherence. This coherence in the AMO’s spatial pattern is also affected by bias adjustment in ERSSTv5 at relative cool (i.e., non-summer) seasons, and by the heterogeneous North Atlantic warming pattern. The different AMO patterns can induce the different effects of wind, surface heat fluxes, and then drive ocean circulation and its heat transport convergence, especially for some seasons. For AMO indices, both the different detrending methods and different observational data result in uncertainty for the period 1935–1950. Such SST uncertainty is important to detect the relative role of the atmosphere and ocean in shaping the AMO.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range between ${M}_{\mathrm{ej}}={10}^{-3}-{10}^{-2}\,{M}_{\odot }$ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

17 WIND ENERGY↗

DOE OSTI2022

Effectively using multifidelity optimization for wind turbine design

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

17 WIND ENERGY↗

NASA NTRS1961

Ascent Problems of Sounding Rockets

Sounding rockets are rocket vehicles designed to carry scientific payloads above the sensible level of the atmosphere. The trajectories of these rockets are very steep, and as a consequence, two important problems arise. These problems are: (1) The dynamic stability of the entire vehicle system as it leaves the atmosphere; (2) The effect of winds on the initial part of the trajectory of such an unguided vehicle. This paper will describe briefly the results of a NASA attempt to cope with these two problems on a particular rocket vehicle.

WIND PROFILE↗

NASA NTRS1963