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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ensemble-based flood vulnerability assessment for probable maximum flood in a changing environment

The magnitude and frequency of hydro-meteorological extremes are expected to increase in a changing environment in ways that threaten the security of US energy-water assets. These include probable maximum precipitation (PMP) and probable maximum flood (PMF), which are used as hydraulic design standards for highly sensitive infrastructures such as nuclear power plants and main dams. To assess the flood vulnerability due to PMP/PMF, an integrated high-resolution process-based hydro-meteorologic modeling framework was used to develop ensemble-based probabilistic flood maps based on best-available historic observations and future climate projections. A graphics processing unit–accelerated 2-dimensional hydrodynamic model was used to simulate the surface inundation areas corresponding to a total of 120 PMF hydrographs. These ensemble-based PMF maps were compared with flood maps obtained from the conventional deterministic PMP/PMF approach, revealing added information about conditional probability of flooding. Further, a relative sensitivity test was conducted to explore the effects of various factors in the framework, such as meteorological forcings, antecedent hydrologic conditions, reservoir storage, and flood model input resolution and parameters. In conclusion, the proposed framework better illustrates the uncertainties associated with model inputs, parameterization, and hydro-meteorological factors, allowing more informed decision-making for future emergency preparation.

58 GEOSCIENCES↗
DOE OSTI2018

Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)

The Amazon forest plays an important role in the tropical circulation and global water and biogeochemical cycles. However, the common Amazon precipitation biases in global climate models undermine our ability to understand future changes in the Amazon and their global consequences. The Community Earth System Model exhibits a persistent wet season dry bias over the southwestern Amazon. The bias is also characterized by inaccurate diurnal timing and intensity of rainfall events. Analysis of a diverse set of simulations suggests that the bias originates from local processes. A known land model bias in the partitioning of surface energy fluxes is found not to contribute significantly to the rainfall bias. Key model processes are identified to be the low-level divergent circulation controlling horizontal moisture flow and the sensitivity of the modeled deep convection to the lower tropospheric moisture. The lower tropospheric moisture divergence dictates the spatiotemporal distributions of convective precipitation through parameterized entrainment mixing, which generally improves the rainfall diurnal cycle. However, it also limits convective precipitation over the areas with frequent and large-scale divergence in the lower troposphere. The southwestern Amazon is such an area, particularly in the late afternoon, although uncertainty exists in the low-level flow in the simulation and reanalyses. One way to sustain rainfall in the presence of low-level divergence is a convective trigger based on both the moisture and temperature changes by large-scale processes, which is shown to help in reducing the dry bias.

Sakaguchi, Koichi↗
DOE OSTI2021

Composited structure of non-precipitating shallow cumulus clouds

The normalized distributions of thermodynamic and dynamical variables both within and outside shallow clouds are investigated through a composite algorithm using large-eddy simulations of oceanic and continental cases. The normalized magnitude is maximum near the cloud centre and decreases outwards. While relative humidity (RH) and cloud liquid water ( q l ) decrease smoothly to match the environment, the vertical velocity, virtual potential temperature (θ v ), and potential temperature (θ) perturbations have more complicated behaviour towards the cloud boundary. Below the inversion layer, θ' v becomes negative before the vertical velocity has turned from an updraft to a subsiding shell outside the cloud, indicating the presence of a transition zone where the updraft is negatively buoyant. Due to the downdraft outside the cloud and enhanced horizontal turbulent mixing across the edge, the normalized turbulent kinetic energy (TKE) and horizontal turbulent kinetic energy (HTKE) decrease more slowly from the cloud centre outwards than the thermodynamic variables. The distributions all present asymmetric structures in response to the vertical wind shear, with more negatively buoyant air, stronger downdrafts, and larger TKE on the downshear side. We discuss several implications of the distributions for theoretical models and parameterizations. Positive buoyancy near the cloud base is mostly due to the virtual effect of water vapour, emphasizing the role of moisture in triggering. The mean vertical velocity is found to be approximately half the maximum vertical velocity within each cloud, providing a constraint to achieve possible power-law distributions for some models. Finally, the normalized distributions for different variables are used to estimate the vertical heat and moisture fluxes within clouds. The results suggest that distributions near the cloud edge and variability of maximum perturbations need careful treatment. The fluxes are underestimated in the inversion layer because cloud-top downdrafts cannot be captured well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Quantifying global N 2 O emissions from natural ecosystem soils using trait-based biogeochemistry models

A group of soil microbes plays an important role in nitrogen cycling and N 2 O emissions from natural ecosystem soils. Wedeveloped a trait-based biogeochemical model based on an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), by incorporating the detailed microbial physiological processes of nitrification. The effect of ammonia-oxidizing Archaea (AOA),ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB) was considered in modeling nitrification. Microbial traits, including microbial biomass and density, were explicitly considered. In addition,nitrogen cycling was coupled with carbon dynamics based on stoichiometry theory between carbon and nitrogen. The model was parameterized using observational data and then applied to quantifying global N 2 O emissions from global terrestrial ecosystem soils from 1990 to 2000. Our estimates of 8.7±1.6 Tg N yr –1 generally agreed with previous estimates during the study period. Tropical forests are a major emitter,accounting for 42 % of the global emissions. The model was more sensitive to temperature and precipitation and less sensitive to soil organic carbon and nitrogen contents. Compared to the model without considering the detailed microbial activities, the new model shows more variations in response to seasonal changes in climate. Our study suggests that further information on microbial diversity and ecophysiology features is needed. The more specific guilds and their traits shall be considered in future soil N 2 O emission quantifications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Relative Biological Effectiveness Variation Along Monoenergetic and Modulated Bragg Peaks of a 62-MeV Therapeutic Proton Beam: A Preclinical Assessment

Purpose: The biological optimization of proton therapy can be achieved only through a detailed evaluation of relative biological effectiveness (RBE) variations along the full range of the Bragg curve. The clinically used RBE value of 1.1 represents a broad average, which disregards the steep rise of linear energy transfer (LET) at the distal end of the spread-out Bragg peak (SOBP). With particular attention to the key endpoint of cell survival, our work presents a comparative investigation of cell killing RBE variations along monoenergetic (pristine) and modulated (SOBP) beams using human normal and radioresistant cells with the aim to investigate the RBE dependence on LET and intrinsic radiosensitvity. Methods and Materials: Human fibroblasts (AG01522) and glioma (U87) cells were irradiated at 6 depth positions along pristine and modulated 62-MeV proton beams at the INFN-LNS (Catania, Italy). Cell killing RBE variations were measured using standard clonogenic assays and were further validated using Monte Carlo simulations and the local effect model (LEM). Results: We observed significant cell killing RBE variations along the proton beam path, particularly in the distal region showing strong dose dependence. Experimental RBE values were in excellent agreement with the LEM predicted values, indicating dose-averaged LET as a suitable predictor of proton biological effectiveness. Data were also used to validate a parameterized RBE model. Conclusions: The predicted biological dose delivered to a tumor region, based on the variable RBE inferred from the data, varies significantly with respect to the clinically used constant RBE of 1.1. The significant RBE increase at the distal end suggests also a potential to enhance optimization of treatment modalities such as LET painting of hypoxic tumors. The study highlights the limitation of adoption of a constant RBE for proton therapy and suggests approaches for fast implementation of RBE models in treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Multilayer interface tracking model of pure tungsten oxidation

Here, we present a numerical model to predict oxide scale growth on tungsten surfaces under exposure to oxygen at high temperatures. The model captures the formation of four thermodynamically-compatible oxide sublayers, WO 2 , WO 2.72 , WO 2.9 , and WO 3 , on top of the metal substrate. Oxide layer growth is simulated by tracking the oxide/oxide and oxide/metal interfaces using a sharp-interface Stefan model coupled to diffusion kinetics. The model is parameterized using selected experimental measurements and electronic structure calculations of the diffusivities of all the oxide subphases involved. We simulate oxide growth at temperatures of 600°C and above, extracting the power law growth exponents in each case, which we find to deviate from classical parabolic growth in several cases. We conduct a comparison of the model predictions with an extensive experimental data set, with reasonable agreement at most temperatures. While many gaps in our understanding still exist, this work is a first attempt at embedding the thermodynamic and kinetic complexity of tungsten oxide growth into a comprehensive mesoscale kinetic model that attempts to capture the essential features of tungsten oxidation to fill existing knowledge gaps and guide and enhance future tungsten oxidation models.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Production and consumption of nitrous oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non-normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments. To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis-Menten (DAMM) model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O 2 ) as substrate or inhibitor for each process. High-frequency chamber-based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate lognormal probability distribution function (PDF) of carbon (C) and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O 2 consumption among microsites. Linking microsite consumption of O 2 with a diffusion model generates a broad range of microsite concentrations of O 2 , which then determines the PDF of microsites that produce or consume CH 4 and N 2 O, such that a range of microsites occurs with both positive and negative signs for net CH 4 and N 2 O flux. Results demonstrate that it is numerically feasible for microsites of N 2 O reduction and CH 4 oxidation to co-occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, however it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2010

Interactions Between Mineral Dust, Climate, and Ocean Ecosystems

Over the past decade, technological improvements in the chemical and physical characterization of dust have provided insights into a number of phenomena that were previously unknown or poorly understood. In addition, models are now incorporating a wider range of physical processes, which will allow us to better quantify the climatic and ecological impacts of dust. For example, some models include the effect of dust on oceanic photosynthesis and thus on atmospheric CO 2 (Friedlingstein et al. 2006). The impact of long-range dust transport, with its multiple forcings and feedbacks, is a relatively new and complex area of research, where input from several disciplines is needed. So far, many of these effects have only been parameterized in models in very simple terms. For example, the representation of dust sources remains a major uncertainty in dust modeling and estimates of the global mass of airborne dust. This is a problem where Earth scientists could make an important contribution, by working with climate scientists to determine the type of environments in which easily erodible soil particles might have accumulated over time. Geologists could also help to identify the predominant mineralogical composition of dust sources, which is crucial for calculating the radiative and chemical effects of dust but is currently known for only a few regions. Understanding how climate and geological processes control source extent and characterizing the mineral content of airborne dust are two of the fascinating challenges in future dust research.

Gasso, Santiago↗
DOE OSTI2020

Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km 2 –0.5°) at which fine-scale (<1km 2 ) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiagvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21 st -century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km 2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Intra-binary shock heating of black widow companions

The low-mass companions of evaporating binary pulsars (black widows and similar) are strongly heated on the side facing the pulsar. However, in high-quality photometric and spectroscopic data, the heating pattern does not match that expected for direct pulsar illumination. Here we explore a model where the pulsar power is intercepted by an intra-binary shock (IBS) before heating the low-mass companion. We develop a simple analytic model and implement it in the popular “ICARUS” light curve code. The model is parameterized by the wind momentum ratio β and the companion wind speed f{sub v}v{sub orb}, and assumes that the reprocessed pulsar wind emits prompt particles or radiation to heat the companion surface. We illustrate an interesting range of light curve asymmetries controlled by these parameters. The code also computes the IBS synchrotron emission pattern, and thus can model black widow X-ray light curves. As a test, we apply the results to the high-quality asymmetric optical light curves of PSR J2215+5135; the resulting fit gives a substantial improvement upon direct heating models and produces an X-ray light curve consistent with that seen. The IBS model parameters imply that at the present loss rate, the companion evaporation has a characteristic timescale of τ{sub evap}≈150 Myr. Still, the model is not fully satisfactory, indicating that there are additional unmodeled physical effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Extending a land-surface model with <i>Sphagnum</i> moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO<sub>2</sub>

Abstract. Mosses need to be incorporated into Earth system models to better simulate peatland functional dynamics under the changing environment. Sphagnum mosses are strong determinants of nutrient, carbon, and water cycling in peatland ecosystems. However, most land-surface models do not include Sphagnum or other mosses as represented plant functional types (PFTs), thereby limiting predictive assessment of peatland responses to environmental change. In this study, we introduce a moss PFT into the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. The model was parameterized and independently evaluated against observations from an ombrotrophic forested bog as part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project. The inclusion of a Sphagnum PFT with some Sphagnum-specific processes in ELM allows it to capture the observed seasonal dynamics of Sphagnum gross primary production (GPP) albeit with an underestimate of peak GPP. The model simulated a reasonable annual net primary production (NPP) for moss but with less interannual variation than observed, and it reproduced aboveground biomass for tree PFTs and stem biomass for shrubs. Different species showed highly variable warming responses under both ambient and elevated atmospheric CO2 concentrations, and elevated CO2 altered the warming response direction for the peatland ecosystem. Microtopography is critical: Sphagnum mosses on hummocks and hollows were simulated to show opposite warming responses (NPP decreasing with warming on hummocks but increasing in hollows), and hummock Sphagnum was modeled to have a strong dependence on water table height. The inclusion of this new moss PFT in global ELM simulations may provide a useful foundation for the investigation of northern peatland carbon exchange, enhancing the predictive capacity of carbon dynamics across the regional and global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Analyzing Production Rate and Carbon Utilization Trade-offs in CO 2 RR Electrolyzers

Carbon utilization is a crucially important performance characteristic of CO 2 electrolyzers; however, applications of simple and accurate descriptive models to experimental data on this topic are currently lacking. Here, we apply a simple analytical reactor model to parameterize single-pass conversion as a function of feed-gas flow rate and show that it captures a wide body of experimental data in the literature exceptionally well. In doing so, we demonstrate that this simple conceptual approach can characterize progress and capture recent innovations in reactor design. To validate the extracted parameters, we employ a well-established comprehensive model to confirm that the results agree with physical expectations. Lastly, we explore the implications of the descriptive model by examining the inherent trade-off between single-pass conversion efficiency and reactor productivity. Understanding such trade-offs is crucially important for advancing this electrolyzer technology toward commercial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Storm-driven particulate organic matter flux connects a tidal tributary floodplain wetland, mainstem river, and estuary

The transport of terrestrial plant matter into coastal waters is important to regional and global biogeochemical cycles, and methods for assessing and predicting fluxes in such dynamic environments are needed. We investigated the hypothesis that upon reconnection of a floodplain wetland to its mainstem river, organic matter produced in the wetland would reach other parts of the ecosystem. If so, we can infer that the organic matter would ultimately become a source for the food web in the mainstem river and estuary. To accomplish this, we adapted numerical hydrodynamic and transport modeling methods to estimate the mass of particulate organic matter (POM) derived from the annually senescent aboveground parts of herbaceous marsh plants (H–POM). The Finite–Volume Community Ocean Model (FVCOM), parameterized with flow, tide, and aboveground biomass data, simulated H–POM mobilization from fluid shear stress during tidal exchange, flooding, and variable river flow; entrainment into the water column; transport via channel and overland flow; and entrapment when wetted surfaces dry. We examined export from a recently reconnected, restoring tidal emergent marsh on the Grays River, a tributary to the Columbia River estuary. Modeling indicated that hydrologically reconnecting 65 ha at the site resulted in export of about 96 × 10 3 kg of H–POM, primarily during pulsed storm flooding events in autumn and early winter. This exported mass amounted to about 19% of the summer peak aboveground biomass measured at the site. Of that 19%, about 48% (47 × 10 3 kg) was deposited downstream in the Grays River and floodplain wetlands, and the remaining 52% (50 × 10 3 kg) passed the confluence of the Grays River and the mainstem estuary located about 7 km from the study site. The colonization of the restoring study site largely by nonnative Phalaris arundinacea (reed canarygrass) may have resulted in 18–28% lower H–POM mobilization than typical marsh plant communities on this floodplain, based on estimates from regional studies of marshes dominated by less recalcitrant species. Here, we concluded that restored floodplain wetlands can contribute significant amounts of organic matter to the estuarine ecosystem and thereby contribute to the restoration of historical trophic structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

A Membrane Model from Implicit Elasticity Theory. Application to Visceral Pleura

A Fungean solid is derived for membranous materials as a body defined by isotropic response functions whose mathematical structure is that of a Hookean solid where the elastic constants are replaced by functions of state derived from an implicit, thermodynamic, internal energy function. The theory utilizes Biot’s (Lond Edinb Dublin Philos Mag J Sci 27:468–489, 1939) definitions for stress and strain that, in one-dimension, are the stress/strain measures adopted by Fung (Am J Physiol 28:1532–1544, 1967) when he postulated what is now known as Fung’s law. Our Fungean membrane model is parameterized against a biaxial data set acquired from a porcine pleural membrane subjected to three, sequential, proportional, planar extensions. These data support an isotropic/deviatoric split in the stress and strain-rate hypothesized by our theory. These data also demonstrate that the material response is highly nonlinear but, otherwise, mechanically isotropic. These data are described reasonably well by our otherwise simple, four-parameter, material model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The effect of stress on the migration of He gas bubbles under a thermal gradient in Fe by phase-field modeling

Here a phase-field model is parameterized to study the effect of elastic stresses on the migration of He gas bubbles in Fe under a temperature gradient. Stresses caused by the gas bubble pressure and residual stress in the Fe matrix are considered. The dependence of He bubble migration velocity on the magnitude of the residual stress, average temperature, temperature gradient, and bubble size is measured. In agreement with a theoretical model based on surface diffusion, simulation results demonstrate that He bubbles move towards the high temperature region with velocities in Fe that are orders of magnitude faster than previously reported in UO 2 . It is found that local stresses in the matrix caused by the He bubble have negligible effect on the bubble migration process; however, residual stresses in the Fe matrix, potentially caused by processing or irradiation, can modestly modify bubble kinetics through pressure dependence of the He diffusion coefficients. Compressive residual stress decreases diffusion coefficients for bulk and surface diffusion mechanisms, thus reducing the migration velocity of the gas bubble. In contrast, tensile residual stress increases the diffusion coefficients, resulting in an increase in the gas bubble migration velocity. This pressure dependence is also consistent with a theoretical model. This phase field model lays the foundation for analysis of bubble coalescence-induced fracture in He bubble-containing steels.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Parameterizing Grid-Averaged Longwave Fluxes for Inhomogeneous Marine Boundary Layer Clouds

This paper examines the relative impacts on grid-averaged longwave flux transmittance (emittance) for Marine Boundary Layer (MBL) cloud fields arising from horizontal variability of optical depth tau and cloud sides, First, using fields of Landsat-inferred tau and a Monte Carlo photon transport algorithm, it is demonstrated that mean all-sky transmittances for 3D variable MBL clouds can be computed accurately by the conventional method of linearly weighting clear and cloudy transmittances by their respective sky fractions. Then, the approximations of decoupling cloud and radiative properties and assuming independent columns are shown to be adequate for computation of mean flux transmittance. Since real clouds have nonzero geometric thicknesses, cloud fractions A'(sub c) presented to isotropic beams usually exceed the more familiar vertically projected cloud fractions A(sub c). It is shown, however, that when A(sub c)less than or equal to 0.9, biases for all-sky transmittance stemming from use of A(sub c) as opposed to A'(sub c) are roughly 2-5 times smaller than, and opposite in sign to, biases due to neglect of horizontal variability of tau. By neglecting variable tau, all-sky transmittances are underestimated often by more than 0.1 for A(sub c) near 0.75 and this translates into relative errors that can exceed 40% (corresponding errors for all-sky emittance are about 20% for most values of A(sub c). Thus, priority should be given to development of General Circulation Model (GCM) parameterizations that account for the effects of horizontal variations in unresolved tau, effects of cloud sides are of secondary importance. On this note, an efficient stochastic model for computing grid-averaged cloudy-sky flux transmittances is furnished that assumes that distributions of tau, for regions comparable in size to GCM grid cells, can be described adequately by gamma distribution functions. While the plane-parallel, homogeneous model underestimates cloud transmittance by about an order of magnitude when 3D variable cloud transmittances are less than or equal to 0.2 and by approx. 20% to 100% otherwise, the stochastic model reduces these biases often by more than 80%.

Barker, Howard W.↗
DOE OSTI2022

Quasar UV Luminosity Function at 3.5 < z < 5.0 from SDSS Deep Imaging Data

We present a well-designed sample of more than 1000 type 1 quasars at 3.5 < z < 5 and derive UV quasar luminosity functions (QLFs) in this redshift range. These quasars were selected using the Sloan Digital Sky Survey (SDSS) imaging data in the Stripe 82 and overlap regions with repeat imaging observations that are about 1 mag fainter than the SDSS single-epoch data. The follow-up spectroscopic observations were conducted by the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) as one of the BOSS ancillary programs. Reaching i ~ 21.5 mag, our sample bridges previous samples from brighter and deeper surveys. We use a 1/V a method to derive binned QLFs at 3.6 < z < 4.0, 4.0 < z < 4.5, and 4.5 < z < 4.9 and then use a double power-law model to parameterize the QLFs. We also combine our data with literature QLFs to better constrain the QLFs across a much wider luminosity baseline. The faint- and bright-end slopes of the QLFs in this redshift range are around –1.7 and –3.7, respectively, with uncertainties from 0.2 to 0.3 to >0.5. The evolution of the QLFs from z ~ 5 to 3.5 can be described by a pure density evolution model (∝10 kz ) with a parameter k similar to that at 5 < z < 7, suggesting a nearly uniform evolution of the quasar density at z = 3.5–7.

79 ASTRONOMY AND ASTROPHYSICS↗