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Results for “distributed inference”

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 415 records · Page 23

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NASA NTRS2006

Pressure Gradient Effects on Hypersonic Cavity Flow Heating

The effect of a pressure gradient on the local heating disturbance of rectangular cavities tested at hypersonic freestream conditions has been globally assessed using the two-color phosphor thermography method. These experiments were conducted in the Langley 31-Inch Mach 10 Tunnel and were initiated in support of the Space Shuttle Return-To-Flight Program. Two blunted-nose test surface geometries were developed, including an expansion plate test surface with nearly constant negative pressure gradient and a flat plate surface with nearly zero pressure gradient. The test surface designs and flow characterizations were performed using two-dimensional laminar computational methods, while the experimental boundary layer state conditions were inferred using the measured heating distributions. Three-dimensional computational predictions of the entire model geometry were used as a check on the design process. Both open-flow and closed-flow cavities were tested on each test surface. The cavity design parameters and the test condition matrix were established using the computational predictions. Preliminary conclusions based on an analysis of only the cavity centerline data indicate that the presence of the pressure gradient did not alter the open cavity heating for laminar-entry/laminar-exit flows, but did raise the average floor heating for closed cavities. The results of these risk-reduction studies will be used to formulate a heating assessment of potential damage scenarios occurring during future Space Shuttle flights.

Everhart, Joel L.↗

NASA NTRS2007

Pressure Gradient Effects on Hypersonic Cavity Flow Heating

Everhart, Joel L.↗

NASA NTRS2010

Aero-Heating of Shallow Cavities in Hypersonic Freestream Flow

The purpose of these experiments and analysis was to augment the heating database and tools used for assessment of impact-induced shallow-cavity damage to the thermal protection system of the Space Shuttle Orbiter. The effect of length and depth on the local heating disturbance of rectangular cavities tested at hypersonic freestream conditions has been globally assessed using the two-color phosphor thermography method. These rapid-response experiments were conducted in the Langley 31-Inch Mach 10 Tunnel and were initiated immediately prior to the launch of STS-114, the initial flight in the Space Shuttle Return-To-Flight Program, and continued during the first week of the mission. Previously-designed and numerically-characterized blunted-nose baseline flat plates were used as the test surfaces. Three-dimensional computational predictions of the entire model geometry were used as a check on the design process and the two-dimensional flow assumptions used for the data analysis. The experimental boundary layer state conditions were inferred using the measured heating distributions on a no-cavity test article. Two test plates were developed, each containing 4 equally-spaced spanwise-distributed cavities. The first test plate contained cavities with a constant length-to-depth ratio of 8 with design point depth-to-boundary-layer-thickness ratios of 0.1, 0.2, 0.35, and 0.5. The second test plate contained cavities with a constant design point depth-to-boundary-layer-thickness ratio of 0.35 with length-to-depth ratios of 8, 12, 16, and 20. Cavity design parameters and the test condition matrix were established using the computational predictions. Preliminary results indicate that the floor-averaged Bump Factor (local heating rate nondimensionalized by upstream reference) at the tested conditions is approximately 0.3 with a standard deviation of 0.04 for laminar-in/laminar-out conditions when the cavity length-to-boundary-layer thickness is between 2.5 and 10 and for cavities in the depth-to-boundary-layer-thickness range of 0.3 to 0.8. Over this same range of conditions and parameters, preliminary results also indicate that the maximum Bump Factor on the cavity centerline falls between 2.0 and 2.75, as long as the cavity-exit conditions remain laminar. Cavities with length-to-boundary-layer-thickness ratio less than 2.5 can not be easily classified with this approach and require further analysis.

Everhart, Joel L.↗

NASA NTRS2013

Herschel's "Cold Debris Disks": Background Galaxies or Quiescent Rims of Planetary Systems?

Infrared excesses associated with debris disk host stars detected so far peak at wavelengths around approx, 100 micron or shorter. However, 6 out of 31 excess sources studied in the Herschel Open Time Key Programme, DUNES, have been seen to show significant-and in some cases extended-excess emission at 160 micron, which is larger than the 100 micron excess. This excess emission has been attributed to circumstellar dust and has been suggested to stem from debris disks colder than those known previously. Since the excess emission of the cold disk candidates is extremely weak, challenging even the unrivaled sensitivity of Herschel, it is prudent to carefully consider whether some or even all of them may represent unrelated galactic or extragalactic emission, or even instrumental noise. We re-address these issues using several distinct methods and conclude that it is highly unlikely that none of the candidates represents a true circumstellar disk. For true disks, both the dust temperatures inferred from the spectral energy distributions and the disk radii estimated from the images suggest that the dust is nearly as cold as a blackbody. This requires the grains to be larger than approx. 100 micron, even if they are rich in ices or are composed of any other material with a low absorption in the visible. The dearth of small grains is puzzling, since collisional models of debris disks predict that grains of all sizes down to several times the radiation pressure blowout limit should be present. We explore several conceivable scenarios: transport-dominated disks, disks of low dynamical excitation, and disks of unstirred primordial macroscopic grains. Our qualitative analysis and collisional simulations rule out the first two of these scenarios, but show the feasibility of the third one. We show that such disks can indeed survive for gigayears, largely preserving the primordial size distribution. They should be composed of macroscopic solids larger than millimeters, but smaller than a few kilometers in size. If larger planetesimals were present, then they would stir the disk, triggering a collisional cascade and thus causing production of small debris, which is not seen. Thus, planetesimal formation, at least in the outer regions of the systems, has stopped before "cometary" or "asteroidal" sizes were reached.

Herschel↗

NASA NTRS2014

Hi-C Observations of Penumbral Bright Dots: Comparison with the IRIS Results

We observed bright dots (BDs) in a sunspot penumbra by using data acquired by the High Resolution Coronal Imager (Hi-C). The sizes of these BDs are on the order of 1 arcsecond (1') and are therefore hard to identify using the Atmospheric Imaging Assembly's (AIA) 0.6' pixel -1 resolution. These BDs become readily apparent with Hi-C's 0.1' pixel -1 resolution. Tian et al. (2014) found penumbral BDs in the transition region (TR) by using the Interface Region Imaging Spectrograph (IRIS). However, only a few of their dots could be associated with any enhanced brightness in AIA channels. In this work, we examine the characteristics of the penumbral BDs observed by Hi-C in a sunspot penumbra, including their sizes, lifetimes, speeds, and intensity. We also attempt to relate these BDs to the IRIS BDs. There are fewer Hi-C BDs in the penumbra than seen by IRIS, though different sunspots were studied and Hi-C had a short observation time. We use 193 A Hi-C data from July 11, 2012 which observed from 18:52:00 UT{18:56:00 UT and supplement it with data from AIA's 193 A passband to see the complete lifetime of the dots that were born before and/or lasted longer than Hi-C's 5-minute observation period. We use additional AIA passbands and compare the light curves of the BDs at different temperatures to test whether the Hi-C BDs are TR BDs. We find that most Hi-C BDs show clear movement, and of those that do, they move in a radial direction, toward or away from the sunspot umbra, sometimes doing both. BDs interact with other BDs, combining to fade away or brighten. The BDs that do not interact with other BDs tend to move less and last longer. We examine the properties of the Hi-C BDs and compare them with the IRIS BDs. Our BDs are similar to the exceptional values of the IRIS BDs: they move slower on average and their sizes and lifetimes are on the higher end of the distributions of IRIS BDs. We infer that our penumbral BDs are some of the larger BDs observed by IRIS.

Alpert, S. E.↗

NASA NTRS2019

Measurement of the Splashback Feature Around SZ-Selected Galaxy Clusters With DES, SPT, and ACT

We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected red MaPPer clusters with similar mass and redshift distributions,rspis∼2σsmaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first in fall into the cluster.

T Shin↗

NASA NTRS2022

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

The primary goal of the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite is to infer precipitationrate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: 1)description of the DPR retrieval algorithm that uses an adjustable relationship between rainrate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dmsimultaneously; 2) evaluation of the DPR algorithm based on the physical simulationsthat employ measured DSD/PSD to understand the mechanism and errorcharacteristics of the retrieval method; 3) review of ground validation studies for theDPR product as well as analysis of the strengths and weaknesses of the ground radarand rain gauge/disdrometer validations. Overall, the DPR Version-6 algorithmprovides reasonably accurate estimates of R and Dm in rain. Non-uniformity in therain profile, however, tends to degrade the accuracy of the R and Dm estimates tosome extent as the range-independent assumption of the adjustable parameter () ofthe R-Dm relation is not able to fully account for natural variation of DSD in the verticalprofile. Underestimation of the DPR snow rate is found when compared with theindependent dual-frequency ratio (DFR) technique. This is possibly the result of theconstraint associated with the path integral attenuation (PIA)/differential PIA (dPIA)used in the DPR algorithm to find the best  and range-independent  assumption. Arange-variable  model, proposed in the DPR Version-7 algorithm, is expected toimprove rain and snow retrieval.

Liang Liao↗

NASA NTRS2022

The Population of Galactic Centre Filaments – III. Candidate Radio and Stellar Sources

Recent MeerKAT radio continuum observations of the Galactic Centre at 20 cm show a large population of non-thermal radio filaments (NRFs) in the inner few hundred pc of the Galaxy. We have selected a sample of 57 radio sources, mainly compact objects, in the MeerKAT mosaic image that appear to be associated with NRFs. The selected sources are about four times the number of radio point sources associated with filaments than would be expected by random chance. Furthermore, an apparent correlation between bright IR stars and NRFs is inferred from their similar latitude distributions, suggesting that they both co-exist within the same region. To examine if compact radio sources are related to compact IR sources, we have used archival 2MASS, and Spitzer data to make spectral energy distribution of individual stellar sources coincident or close to radio sources. We provide a catalogue of radio and IR sources for future detailed observations to investigate a potential three-way physical association between NRFs, compact radio and IR stellar sources. This association is suggested by models in which NRFs are cometary tails produced by the interaction of a large-scale nuclear outflow with stellar wind bubbles in the Galactic Centre.

plasmas↗

DOE OSTI2026

Peering down the barrel with DESI DR2: 10 000+ inflows at $z$ < 0.6 reveal how galaxies accrete cold gas

Direct observational constraints on how galaxies acquire their gas remain remarkably limited, hindering our understanding of the baryon cycle. We present a search for down-the-barrel NaI D absorption towards 15.6 million galaxies at $z < 0.6$ in DESI Data Release 2. We use Bayesian evidence ratios to assess whether the absorption requires additional components tracing interstellar gas distinct from the systemic component of the galaxy. We construct a catalogue of 50 088 (27 420) galaxies with moderate (strong) evidence for down-the-barrel absorption. The inferred absorption components are broadly distributed in velocity, with approximately 50% at $v_{\rm flow} < -50$ km/s, 30% within 50 km/s of the systemic velocity and the remaining 20% at $v_{\rm flow} > 50$ km/s. We find strong evidence for a large population of low-velocity, infalling absorbers with velocities $\sim$20 km/s in edge-on galaxies, consistent with radial inflows predicted in simulations. The stronger correlation in early-type galaxies between inflow velocity and stellar velocity dispersion, compared to that with stellar mass, suggests that a portion of these inflows may be associated with accreting satellites. These results reveal the multiple pathways in which galaxies accrete gas at redshift $z < 0.6$ for the first time in a statistically significant sample.

Weng, S. [Marseille, Lab. Astrophys.]↗

DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗

DOE OSTI2026

Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2D SOL models

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.

Bayesian optimization↗

DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗

NASA NTRS1994

On the prediction of velocity fields from redshift space galaxy samples

We present a new method for recovering the underlying velocity field from an observed distribution of galaxies in redshift space. The method is based on a kinematic Zel'dovich relation between the velocity and density fields in redshift space. This relation is expressed in a differential equation slightly modified from the usual Poisson equation and which depends nontrivially on Beta identically equal to Omega(exp 0.6)/b. The linear equation can be readily solved by standard techniques of separation of variables by means of spherical harmonics. One can also include a term describing the 'rocket effect' discussed by Kaiser (1987). From this redshift space information alone, one can generate a prediction of the peculiar velocity field for each harmonic (l, m) as a function of distance. We note that for the quadrupole and higher order moments, the equation is a boundary value problem with solutions dependent on both the interior and exterior mass distribution. However, for a shell at distance r, the dipole, as well as the monopole, of the velocity field in the Local Group frame is fully determined by the interior mass distribution. This implies that the shear of the measured velocity field, when fitted to a dipole distortion, should be aligned and consistent with the gravity field inferred from the well determined local galaxy distribution. As a preliminary application we compute the velocity dipole of distant shells as predicted from the 1.2 Jy IRAS survey compared to the measured velocity dipole on shells, as inferred from a recent POTENT analysis. The coherence between the two fields is good, yielding a best estimate of Beta = 0.6 +/- 0.2.

Nusser, Adi↗

DOE OSTI2025

Bayesian inference of nuclear-matter density from proton scattering

Background: Proton elastic scattering at intermediate energy is widely employed as a tool for determining the matter radius of atomic nuclei. Here, the sensitivity of the approach relies on high-resolution measurements at small scattering angles and low-momentum transfer. Under these conditions, the Glauber multiple scattering theory accurately describes the proton-nucleus elastic cross section. Purpose: Investigate the sensitivity of the Glauber multiple scattering theory to uncertainties associated with input parameters such as the nuclear-matter density distribution and nucleon-nucleon data. Method: A joint Bayesian inference was performed using 12 angular distributions of elastic scattering at different energies on 58 Ni, 90 Zr, and 208 Pb targets. A Metropolis-Hastings algorithm was implemented to make an uncertainty quantification analysis for the input parameters used in the Glauber multiple scattering theory. Results: The experimental cross sections were fitted simultaneously using a joint Bayesian inference approach. Posterior probability density distributions of 42 input parameters were obtained from the analysis. A moderate correlation between the nuclear density parameters and the nucleon-nucleon cross sections was found. This correlation impacts the extraction of the nuclear-matter radius. Conclusions: The present analysis provided a consistent method for extracting the nuclear-matter density distribution of 58 Ni, 90 Zr, and 208 Pb from data across different incident energies. Due to the correlation of the nucleon-nucleon cross sections with the other input parameters, a constrained Bayesian inference using free nucleon-nucleon cross section data was performed. The nuclear-matter radii obtained from the analysis are in good agreement with multiple results reported in the literature.

190 ≤ A ≤ 219↗

NASA NTRS2011

Cloud Condensation in Titan's Lower Stratosphere

A 1-D condensation model is developed for the purpose of reproducing ice clouds in Titan's lower stratosphere observed by the Composite Infrared Spectrometer (CIRS) onboard Cassini. Hydrogen cyanide (HCN), cyanoacetylene (HC3N), and ethane (C2H6) vapors are treated as chemically inert gas species that flow from an upper boundary at 500 km to a condensation sink near Titan's tropopause (-45 km). Gas vertical profiles are determined from eddy mixing and a downward flux at the upper boundary. The condensation sink is based upon diffusive growth of the cloud particles and is proportional to the degree of supersaturation in the cloud formation regIOn. Observations of the vapor phase abundances above the condensation levels and the locations and properties of the ice clouds provide constraints on the free parameters in the model. Vapor phase abundances are determined from CIRS mid-IR observations, whereas cloud particle sizes, altitudes, and latitudinal distributions are derived from analyses of CIRS far-IR observations of Titan. Specific cloud constraints include: I) mean particle radii of2-3 J.lm inferred from the V6 506 cm- band of HC3N, 2) latitudinal abundance distributions of condensed nitriles, inferred from a composite emission feature that peaks at 160/cm , and 3) a possible hydrocarbon cloud layer at high latitudes, located near an altitude of 60 km, which peaks between 60 and 80 cm l . Nitrile abundances appear to diminish substantially at high northern latitudes over the time period 2005 to 2010 (northern mid winter to early spring). Use of multiple gas species provides a consistency check on the eddy mixing coefficient profile. The flux at the upper boundary is the net column chemical production from the upper atmosphere and provides a constraint on chemical pathways leading to the production of these compounds. Comparison of the differing lifetimes, vapor phase transport, vapor phase loss rate, and particle sedimentation, sheds light on temporal stability of the clouds.

Romani, Paul N.↗