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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In Search of a Cost-Effective Approach for Estimating Dam Passage Survival

Abstract Passage of downstream-migrating fishes through hydropower dams poses a management issue in rivers throughout the world. As such, regulations have been enacted at many locales to understand and limit fish losses by requiring dam passage survival estimation. However, limiting estimates of survival to the dam and immediate tailrace presents a challenge to researchers. In this study, two alternative release–recapture methods were used to estimate dam passage survival of juvenile salmonids through Lower Granite Dam on the Snake River, Washington, in 2018. One approach, the virtual/paired-release (ViPRe) model, was extensively used in the Columbia–Snake River basin at federally operated hydroelectric dams during 2010–2014. This existing approach uses three releases of tagged smolts to isolate dam passage survival, defined as survival from the upstream dam face to the tailrace mixing zone 1–2 km downstream of the dam. An alternative approach, the virtual release/dead-fish correction (ViRDCt) model, uses one live-release group paired with a release of dead tagged fish at the dam to estimate the same survival parameter. The alternative estimation approaches were tested on two spring out-migrating stocks, yearling Chinook Salmon Oncorhynchus tshawytscha and steelhead Oncorhynchus mykiss, and on a summer out-migrating stock of subyearling Chinook Salmon. The alternative estimates for these stocks were all within 1 SE. However, the ViRDCt model produced survival estimates with SEs that were 59% smaller than those of the existing ViPRe model and did so using 42% fewer tagged fish and one less downstream acoustic detection array. Because of the reduced sample size, nearly US$540,000 less in tag costs and 4,000 fewer labor-hours were required to implement the ViRDCt model compared to the ViPRe model. As such, the ViRDCt model represents a cost-effective and precise approach to characterizing dam passage survival.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3 ) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5) and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3 . Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3 -related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3 -related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Uncertainty quantification for radiation measurements: Bottom-up error variance estimation using calibration information

One example of top-down uncertainty quantification (UQ) involves comparing two or more measurements on each of multiple items. One example of bottom-up UQ expresses a measurement result as a function of one or more input variables that have associated errors, such as a measured count rate, which individually (or collectively) can be evaluated for impact on the uncertainty in the resulting measured value. In practice, it is often found that top-down UQ exhibits larger error variances than bottom-up UQ because some error sources are present in the fielded assay methods used in top-down UQ that are not present (or not recognized) in the assay studies used in bottom-up UQ One would like better consistency between the two approaches in order to claim understanding of the measurement process. The purpose of this paper is to refine bottom-up uncertainty estimation by using calibration information so that if there are no unknown error sources, the refined bottom-up uncertainty estimate will agree with the top-down uncertainty estimate to within a specified tolerance. Then, in practice, if the top-down uncertainty estimate is larger than the refined bottom-up uncertainty estimate by more than the specified tolerance, there must be omitted sources of error beyond those predicted from calibration uncertainty. The paper develops a refined bottom-up uncertainty approach for four cases of simple linear calibration: (1) inverse regression with negligible error in predictors, (2) inverse regression with non negligible error in predictors, (3) classical regression followed by inversion with negligible error in predictors, and (4) classical regression followed by inversion with non-negligible errors in predictors. Our illustrations are of general interest, but are drawn from our experience with nuclear material assay by non-destructive assay. The main example we use is gamma spectroscopy that applies the enrichment meter principle. Previous papers that ignore error in predictors have shown a tendency for inverse regression to have lower error variance than classical regression followed by inversion. Our paper supports that tendency both with and without error in predictors. Furthermore, the paper shows that calibration parameter estimates using error in predictor methods perform worse than without using error in predictor methods in the case of inverse regression, but perform better than without using error in predictor methods in the case of classical regression followed by inversion. Both inverse and classical regression involve the ratio of dependent random variables; therefore, the assumed error distribution(s) will matter in parameter estimation and in uncertainty calculations. Mainly for that reason, calibration using a single predictor is distinct from simple regression, and it has not been thoroughly treated in the literature, nor in the ISO Guide to the Expression of Uncertainty in Measurements (GUM). This refined approach is based on simulation, because we illustrate that analytical approximations are not adequate when there are, for example, 10 or fewer calibration measurements, which is common in calibration applications, each consisting of measured responses from known quantities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or iterative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power afforded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. Here, we verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from continuous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases to relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Parametric estimation of Poisson's ratio for thin hinged-hinged plates

Cornu's method is an elegant calculation besieged by an impractical approach to obtain accurate estimates for Poisson's ratio. Conventionally, Cornu's method requires several components, and each component can adversely affect the accuracy of the measurement. Furthermore, Cornu's conventional method requires a long beam because beams with short length-to-width ratios cause the estimate of Poisson's ratio to diverge from the true value of Poisson's ratio. We believe that, with the right modifications, Cornu's method can become an attractive approach to obtaining precise estimates for Poisson's ratio from mode shapes. Here we use finite element simulations to show how to use Cornu's method to estimate Poisson's ratio from a mode shape. Our modified Cornu's method removes knife-edges and loading components for a hinged-hinged plate under steady-state excitation. Given the true value of Poisson's ratio, as a performance specification, we show that simple parametric expressions can fit estimates for Poisson's ratio for different length-to-width ratios of thin hinged-hinged plates. Additionally, we show that estimates for Poisson's ratio from higher modes align with the results from the first mode and explain our expectation for this outcome. Furthermore, our results challenge the idea that anticlastic, monoclastic, and synclastic deformation uniquely correspond to positive, zero, and negative estimates of Poisson's ratio, respectively. With the emergence of materials-by-design, we expect that this parametric technique will be able to assist in experimental qualification of thin beam and plate structures with respect to the desired value of Poisson's ratio.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
DOE OSTI2013

How Radiation Oncologists Evaluate and Incorporate Life Expectancy Estimates Into the Treatment of Palliative Cancer Patients: A Survey-Based Study

Purpose: We surveyed how radiation oncologists think about and incorporate a palliative cancer patient’s life expectancy (LE) into their treatment recommendations. Methods and Materials: A 41-item survey was e-mailed to 113 radiation oncology attending physicians and residents at radiation oncology centers within the Boston area. Physicians estimated how frequently they assessed the LE of their palliative cancer patients and rated the importance of 18 factors in formulating LE estimates. For 3 common palliative case scenarios, physicians estimated LE and reported whether they had an LE threshold below which they would modify their treatment recommendation. LE estimates were considered accurate when within the 95% confidence interval of median survival estimates from an established prognostic model. Results: Among 92 respondents (81%), the majority were male (62%), from an academic practice (75%), and an attending physician (70%). Physicians reported assessing LE in 91% of their evaluations and most frequently rated performance status (92%), overall metastatic burden (90%), presence of central nervous system metastases (75%), and primary cancer site (73%) as “very important” in assessing LE. Across the 3 cases, most (88%-97%) had LE thresholds that would alter treatment recommendations. Overall, physicians’ LE estimates were 22% accurate with 67% over the range predicted by the prognostic model. Conclusions: Physicians often incorporate LE estimates into palliative cancer care and identify important prognostic factors. Most have LE thresholds that guide their treatment recommendations. However, physicians overestimated patient survival times in most cases. Future studies focused on improving LE assessment are needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Evaluation of multiple tracer methods to estimate low groundwater flow velocities

Here, four different tracer methods were used to estimate groundwater flow velocity at a multiple-well site in the saturated alluvium south of Yucca Mountain, Nevada: (1) two single-well tracer tests with different rest or “shut-in” periods, (2) a cross-hole tracer test with an extended flow interruption, (3) a comparison of two tracer decay curves in an injection borehole with and without pumping of a downgradient well, and (4) a natural-gradient tracer test. Such tracer methods are potentially very useful for estimating groundwater velocities when hydraulic gradients are flat (and hence uncertain) and also when water level and hydraulic conductivity data are sparse, both of which were the case at this test location. The purpose of the study was to evaluate the first three methods for their ability to provide reasonable estimates of relatively low groundwater flow velocities in such low-hydraulic-gradient environments. The natural-gradient method is generally considered to be the most robust and direct method, so it was used to provide a “ground truth” velocity estimate. However, this method usually requires several wells, so it is often not practical in systems with large depths to groundwater and correspondingly high well installation costs. The fact that a successful natural gradient test was conducted at the test location offered a unique opportunity to compare the flow velocity estimates obtained by the more easily deployed and lower risk methods with the ground-truth natural-gradient method. The groundwater flow velocity estimates from the four methods agreed very well with each other, suggesting that the first three methods all provided reasonably good estimates of groundwater flow velocity at the site. We discuss the advantages and disadvantages of the different methods, as well as some of the uncertainties associated with them.

58 GEOSCIENCES↗
DOE OSTI2018

Estimation of volume-weighted average grain size in Fe-based nanocrystalline soft magnetic materials by autocorrelation function

Highlights: • Normalized ACF of TEM images estimates the volume-weighted average grain size. • Grain size estimation by normalized ACF shows good accuracy and time efficiency. • Normalized ACF can reveal grain size difference in different areas of a TEM sample. • Normalized ACF helps modelling of grain size distributions in Fe-B nanostructures. - Abstract: The coercivity of Fe-based nanocrystalline soft magnetic materials depends critically on the grain size of bcc-Fe grains. Accurately estimating the volume-weighted mean grain size is extremely important to develop new Fe-based nanocrystalline soft magnetic materials with low coercivity. This article shows that autocorrelation analysis of dark-field transmission electron microscope (TEM) images provides a very good estimation of bcc-Fe grain size in nanocrystalline soft magnetic materials. This method can be used to efficiently estimate the volume-weighted average grain size from multiple TEM images with much less inherent bias than manual measurement techniques. The estimated grain sizes show good consistency to the grain sizes estimated by X-ray diffraction (XRD) and manual measurement. In contrast to XRD, this method can also reveal the differences in grain size from different micron-sized regions of a sample.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Estimation of retired mobile phones generation in China: A comparative study on methodology

Highlights: • The sales data of mobile phones in China was revised by considering the amount of smuggled and counterfeit mobile phones. • The estimation of retired mobile phones in China was made by comparing some relevant methods. • The advanced result of estimation can help improve the policy-making. • The method suggested in this paper can be also used in other countries. • Some discussions on methodology are also conducted in order for the improvement. - Abstract: Due to the rapid development of economy and technology, China has the biggest production and possession of mobile phones around the world. In general, mobile phones have relatively short life time because the majority of users replace their mobile phones frequently. Retired mobile phones represent the most valuable electrical and electronic equipment (EEE) in the main waste stream because of such characteristics as large quantity, high reuse/recovery value and fast replacement frequency. Consequently, the huge amount of retired mobile phones in China calls for a sustainable management system. The generation estimation can provide fundamental information to construct the sustainable management system of retired mobile phones and other waste electrical and electronic equipment (WEEE). However, the reliable estimation result is difficult to get and verify. The priority aim of this paper is to provide proper estimation approach for the generation of retired mobile phones in China, by comparing some relevant methods. The results show that the sales and new method is in the highest priority in estimation of the retired mobile phones. The result of sales and new method shows that there are 47.92 million mobile phones retired in 2002, and it reached to 739.98 million in China in 2012. It presents an increasing tendency with some fluctuations clearly. Furthermore, some discussions on methodology, such as the selection of improper approach and error in the input data, are also conducted in order to improve generation estimation of retired mobile phones and other WEEE.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Bayesian Model Averaging for Ensemble-Based Estimates of Solvation Free Energies

This paper applies the Bayesian Model Averaging (BMA) statistical ensemble technique to estimate small molecule solvation free energies. There is a wide range methods for predicting solvation free energies, ranging from empirical statistical models to {\it ab initio} quantum mechanical approaches. Each of these methods are based on a set of conceptual assumptions that can affect a method's predictive accuracy and transferability. Using an iterative statistical process, we have selected and combined solvation energy estimates using an ensemble of 17 diverse methods from the SAMPL4 blind prediction study to form a single, aggregated solvation energy estimate. The ensemble design process evaluates the statistical information in each individual method as well as the performance of the aggregate estimate obtained from the ensemble as a whole. Methods that possess minimal or redundant information are pruned from the ensemble and the evaluation process repeats until aggregate predictive performance can no longer be improved. We show that this process results in a final aggregate estimate that outperforms all individual methods by reducing estimate errors by as much as 91% to 1.2 kcal/mol accuracy. We also compare our iterative refinement approach to other statistical ensemble approaches and demonstrate that this iterative process reduces estimate errors by as much as 61%. This work provides a new approach for accurate solvation free energy prediction and lays the foundation for future work on aggregate models that can balance computational cost with predictive accuracy.

solvation, Bayesian model averaging, Electrostatic↗
DOE OSTI2018

Estimating Precommercial Heterogeneous Catalyst Price: A Simple Step-Based Method

Developers of novel catalysts often have limited access to information on the costs of their catalytic materials. The R&D and commercialization decisions made by these researchers would benefit from cost estimates that are accurate and directly comparable to the prices of incumbent catalytic materials, but developing such estimates requires detailed knowledge of industrial synthesis methods and their costs. In order to address one critical part of this need for cost information, namely, the capital and operating costs of using catalyst manufacturing process equipment, a simple and researcher-accessible 'step method' is described herein. This method is based on the estimating techniques that contract manufacturers of industrial catalysts use to develop price quotes for their services. It allows the development of an estimate of catalyst synthesis costs by selection of individual processing steps from a list. Data and methods to allow estimation of processing cost via the step method are provided for synthesis quantities of 1-1000 tons. Price estimates for three example materials, including Pt/C, Ni/Al 2 O 3 , and fluid catalytic cracking catalysts, are described in detail and shown to compare favorably (within +/-20%) with market prices for those materials. Lastly, the step method is placed in the context of a forthcoming free and publicly available catalyst cost estimation tool that will incorporate raw materials pricing, dedicated catalyst plants for higher-volume syntheses, spent catalyst value, and other features.

09 BIOMASS FUELS↗
DOE OSTI2019

Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

58 GEOSCIENCES↗
DOE OSTI2023

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Abstract Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys . Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient ( r ) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Environmental Sciences & Ecology↗
DOE OSTI2015

Reduced carbon emission estimates from fossil fuel combustion and cement production in China

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China’s total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China’s carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000–2012 than the value reported by China’s national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China’s cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China’s CO 2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China’s cumulative carbon emissions. Our findings suggest that overestimation of China’s emissions in 2000–2013 may be larger than China’s estimated total forest sink in 1990–2007 (2.66 gigatonnes of carbon) or China’s land carbon sink in 2000–2009 (2.6 gigatonnes of carbon).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Explicit approximations to estimate the perturbative diffusivity in the presence of convectivity and damping. I. Semi-infinite slab approximations

In this paper, a number of new approximations are introduced to estimate the perturbative diffusivity (χ), convectivity (V), and damping (τ) in cylindrical geometry. For this purpose, the harmonic components of heat waves induced by localized deposition of modulated power are used. The approximations are based on semi-infinite slab approximations of the heat equation. The main result is the approximation of χ under the influence of V and τ based on the phase of two harmonics making the estimate less sensitive to calibration errors. To understand why the slab approximations can estimate χ well in cylindrical geometry, the relationships between heat transport models in slab and cylindrical geometry are studied. In addition, the relationship between amplitude and phase with respect to their derivatives, used to estimate χ, is discussed. The results are presented in terms of the relative error for the different derived approximations for different values of frequency, transport coefficients, and dimensionless radius. The approximations show a significant region in which χ, V, and τ can be estimated well, but also regions in which the error is large. Also, it is shown that some compensation is necessary to estimate V and τ in a cylindrical geometry. On the other hand, errors resulting from the simplified assumptions are also discussed showing that estimating realistic values for V and τ based on infinite domains will be difficult in practice. This paper is the first part (Part I) of a series of three papers. In Part II and Part III, cylindrical approximations based directly on semi-infinite cylindrical domain (outward propagating heat pulses) and inward propagating heat pulses in a cylindrical domain, respectively, will be treated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

On the estimation of the local dark matter density using the rotation curve of the Milky Way

The rotation curve of the Milky Way is commonly used to estimate the local dark matter density ρDM,⊙. However, the estimates are subject to the choice of the distribution of baryons needed in this type of studies. In this work we explore several Galactic mass models that differ in the distribution of baryons and dark matter, in order to determine ρDM,⊙. For this purpose we analyze the precise circular velocity curve measurement of the Milky Way up to ~ 25 kpc from the Galactic centre obtained from Gaia DR2 [1]. We find that the estimated value of ρDM,⊙ stays robust to reasonable changes in the spherical dark matter halo. However, we show that ρDM,⊙ is affected by the choice of the model for the underlying baryonic components. In particular, we find that ρDM,⊙ is mostly sensitive to uncertainties in the disk components of the Galaxy. We also show that, when choosing one particular baryonic model, the estimate of ρDM,⊙ has an uncertainty of only about 10% of its best-fit value, but this uncertainty gets much bigger when we also consider the variation of the baryonic model. In particular, the rotation curve method does not allow to exclude the presence of an additional very thin component, that can increase ρDM,⊙ by more than a factor of 8 (the thin disk could even be made of dark matter). Therefore, we conclude that exclusively using the rotation curve of the Galaxy is not enough to provide a robust estimate of ρDM,⊙. For all the models that we study without the presence of an additional thin component, our resulting estimates of the local dark matter density take values in the range ρDM,⊙ ≃ 0.3–0.4 GeV/cm 3 , consistent with many of the estimates in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

DESI DR1 Ly α 1D power spectrum: Validation of estimators

The Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) is the largest sample to date for small-scale Lyα forest cosmology, accessed through its one-dimensional power spectrum (P 1D ). The Lyα forest P 1D is extracted from quasar spectra that are highly inhomogeneous (both in wavelength and between quasars) in noise properties due to intrinsic properties of the quasar, atmospheric and astrophysical contamination, and also sensitive to low-level details of the spectral extraction pipeline. We employ two estimators in DR1 analysis to measure P 1D : the optimal estimator and the fast Fourier transform (FFT) estimator. To ensure robustness of our DR1 measurements, we validate these two power spectrum and covariance matrix estimation methodologies against the challenging aspects of the data. First, using a set of 20 synthetic 1D realizations of DR1, we derive the masking bias corrections needed for the FFT estimator and the continuum fitting bias needed for both estimators. We demonstrate that both estimators, including their covariances, are unbiased with these corrections using the Kolmogorov-Smirnov test. Second, we substantially extend our previous suite of CCD image simulations to include 675,000 quasars, allowing us to accurately quantify the pipeline's performance. This set of simulations reveals biases at the highest k values, corresponding to a resolution error of a few percent. We base the resolution systematics error budget of DR1 P 1D on these values, but do not derive corrections from them since the simulation fidelity is insufficient for precise corrections.

Lyman alpha forest↗