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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

High Penetration Solar PV Deployment Sunshine State Solar Grid Initiative (SUNGRIN)

The report provides results from the Sunshine State Solar Grid Initiative (SUNGRIN) high penetration solar PV deployment project led by Florida State University’s (FSU) Center for Advanced Power Systems (CAPS). FSU CAPS and industry and university partners have completed a five-year effort aimed at enabling effective integration of high penetration levels of grid-connected solar PV generation. SUNGRIN has made significant contributions in the development of simulation-assisted techniques, tools, insight and understanding associated with solar PV effects on electric power system (EPS) operation and the evaluation of mitigation options for maintaining reliable operation. An important element of the project was the partnership and participation of six major Florida utilities and the Florida Reliability Coordinating Council (FRCC). Utilities provided details and data associated with actual distribution circuits having high-penetration PV to use as case studies. The project also conducted foundational work supporting future investigations of effects at the transmission / bulk power system level. In the final phase of the project, four open-use models with built-in case studies were developed and released, along with synthetic solar PV data sets, and tools and techniques for model reduction and in-depth parametric studies of solar PV impact on distribution circuits. Along with models and data, at least 70 supporting MATLAB functions have been developed and made available, with complete documentation.

14 SOLAR ENERGY↗
DOE OSTI2013

NEW TECHNIQUES FOR HIGH-CONTRAST IMAGING WITH ADI: THE ACORNS-ADI SEEDS DATA REDUCTION PIPELINE

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to {approx}20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field of view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a Berkeley Software Distribution (BSD) license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Pre-Processing of Cross-Sections Using Dimensionality Reduction Techniques

Reactor physics codes rely on cross-section data to determine, for example, flux profiles or burn-up calculations. This data contains cross section values for different neutron-atom interactions (e.g., absorption, fission and scattering) for a large number of isotopes as function of energy and it is tabulated for a specific set of parameters (e.g., moderator temperature, fuel temperature, moderator density). In order to perform such neutronic calculations, the amount of this data that needs to be stored is very large due to the high number of isotopes and many tabulation points are needed. This data needs to be retrieved at each time iteration of the code from the machine memory which negatively affects the computational time of the code itself.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Improving the precision of astrometry for space debris

The data reduction method for optical space debris observations has many similarities with the one adopted for surveying near-Earth objects; however, due to several specific issues, the image degradation is particularly critical, which makes it difficult to obtain precise astrometry. An automatic image reconstruction method was developed to improve the astrometry precision for space debris, based on the mathematical morphology operator. Variable structural elements along multiple directions are adopted for image transformation, and then all the resultant images are stacked to obtain a final result. To investigate its efficiency, trial observations are made with Global Positioning System satellites and the astrometry accuracy improvement is obtained by comparison with the reference positions. The results of our experiments indicate that the influence of degradation in astrometric CCD images is reduced, and the position accuracy of both objects and stellar stars is improved distinctly. Our technique will contribute significantly to optical data reduction and high-order precision astrometry for space debris.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy

Purpose: To present the implementation and validation of a geometrical based variance reduction technique for the calculation of phase space data for proton therapy dose calculation. Methods: The treatment heads at the Francis H Burr Proton Therapy Center were modeled with a new Monte Carlo tool (TOPAS based on Geant4). For variance reduction purposes, two particle-splitting planes were implemented. First, the particles were split upstream of the second scatterer or at the second ionization chamber. Then, particles reaching another plane immediately upstream of the field specific aperture were split again. In each case, particles were split by a factor of 8. At the second ionization chamber and at the latter plane, the cylindrical symmetry of the proton beam was exploited to position the split particles at randomly spaced locations rotated around the beam axis. Phase space data in IAEA format were recorded at the treatment head exit and the computational efficiency was calculated. Depth-dose curves and beam profiles were analyzed. Dose distributions were compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. In addition, dose in two patients was simulated with and without particle splitting to compare the efficiency and accuracy of the technique. Results: A normalized computational efficiency gain of a factor of 10-20.3 was reached for phase space calculations for the different treatment head options simulated. Depth-dose curves and beam profiles were in reasonable agreement with the simulation done without splitting: within 1% for depth-dose with an average difference of (0.2 {+-} 0.4)%, 1 standard deviation, and a 0.3% statistical uncertainty of the simulations in the high dose region; 1.6% for planar fluence with an average difference of (0.4 {+-} 0.5)% and a statistical uncertainty of 0.3% in the high fluence region. The percentage differences between dose distributions in water for simulations done with and without particle splitting were within the accepted clinical tolerance of 2%, with a 0.4% statistical uncertainty. For the two patient geometries considered, head and prostate, the efficiency gain was 20.9 and 14.7, respectively, with the percentages of voxels with gamma indices lower than unity 98.9% and 99.7%, respectively, using 2% and 2 mm criteria. Conclusions: The authors have implemented an efficient variance reduction technique with significant speed improvements for proton Monte Carlo simulations. The method can be transferred to other codes and other treatment heads.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Optical spectroscopy of molten fluorides: Methods, electronic and vibrational data, structural interpretation, and relevance to radiative heat transfer

In this study, to help address the need for predicting radiative heat transfer (RHT) behavior of molten salts, we conducted a comprehensive review of methods and data from optical spectroscopic measurements on molten fluoride salts. Transmittance, reflectance, and trans-reflectance experimental methods are discussed, along with the corresponding data reduction methodology and the limitations of each technique. Optical spectroscopy is a convenient indirect probe for changes in structural parameters with temperature and composition. Electronic and vibrational absorption data for transition-metal, lanthanide, and actinide solutes and vibrational absorption data for alkali and alkaline earth fluoride solvents are compiled, and the corresponding structural interpretation is discussed and compared with other experimental and theoretical work. We find that solvent and solute vibrational absorption can be significant in the mid-infrared, resulting in near-infrared edges of significance to RHT. Extrapolation and averaging of existing edge data leads to estimated gray absorption coefficient values at 700 °C of 546 m —1 for FLiBe and 276 m —1 for FLiNaK, both within the range of 1 – 6000 m —1 identified to be of engineering relevance for radiative heat transfer analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Data–driven model reduction for the Bayesian solution of inverse problems

One of the prime challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper presents a data-driven projection-based model reduction technique to reduce this computational cost. The proposed technique has two distinctive features. To begin, the model reduction strategy is tailored to inverse problems: the snapshots used to construct the reduced-order model are computed adaptively from the posterior distribution. Posterior exploration and model reduction are thus pursued simultaneously. Second, to avoid repeated evaluations of the full-scale numerical model as in a standard MCMC method, we couple the full-scale model and the reduced-order model together in the MCMC algorithm. This maintains accurate inference while reducing its overall computational cost. In numerical experiments considering steady-state flow in a porous medium, the data-driven reduced-order model achieves better accuracy than a reduced-order model constructed using the classical approach. It also improves posterior sampling efficiency by several orders of magnitude compared with a standard MCMC method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Characterization and thermal behavior of PrMO{sub 3} (M = Co or Ni) ceramic materials obtained from gelatin

Graphical abstract: The micrograph in figure shows sample calcined at temperature 900 °C. The sample exhibits morphology with considerable porosity and the formation of agglomerated nanometric particles. Gelatin provides the system with a large amount of organic matter, which is then removed during calcinations, favoring the appearance of pores in the material. Highlights: ► Oxides with PrNiO{sub 3} and PrCoO{sub 3} were prepared by new method synthesis. ► The gelatin, through its carboxylate groups and amine, is an efficient director. ► The obtained materials have magnetic properties and application in catalysis. ► The decomposition kinetic study of bonding groups of gelatin with metallic ions that takes part in the synthesis of PrMO{sub 3}. -- Abstract: Metal oxides with perovskite-type structure have attracted considerable interest in recent years due to their magnetic and electrical properties, as well as their catalytic activity. In this study, oxides with PrNiO{sub 3} and PrCoO{sub 3} composition were prepared by using gelatin powder as a precursor agent for its use as a catalyst. The powders obtained were calcined at 700 °C and 900 °C and characterized using the X-ray diffraction, thermal analysis (thermogravimetry and differential thermal analysis), infrared spectroscopy, temperature programed reduction and scanning electron microscopy techniques. Thermogravimetric data using the non-isothermal kinetic models of Flynn and Wall and “Model-free Kinetics” were used to determine the activation energy to study the decomposition kinetics of the ligand groups with system's metallic ions that takes part in the synthesis of PrMO{sub 3} (M = Ni or Co).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques

Brillouin Optical Time Domain Analysis (BOTDA) sensor system play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. Nonetheless, challenges emerge as distances grow due to signal attenuation and noise interference resulting in measurement errors. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond 10’s of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. This approach proves particularly advantageous in BOTDA systems, where accurate measurement of Brillouin scattering signals is paramount for long-range sensing, while concurrently safeguarding high spatial resolutions. In summary, this research has shown a holistic exploration of extending BOTDA's distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.

Bhatta, Hari↗
DOE OSTI2020

PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python

Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy weather forecast. With the advance in measurement systems, as well as the reduction in sensor prices, data assimilation (DA) techniques are applicable to various fields, other than meteorology. However, beginners usually face hardships digesting the core ideas from the available sophisticated resources requiring a steep learning curve. In this tutorial, we lay out the mathematical principles behind DA with easy-to-follow Python module implementations so that this group of newcomers can quickly feel the essence of DA algorithms. We explore a series of common variational, and sequential techniques, and highlight major differences and potential extensions. We demonstrate the presented approaches using an array of fluid flow applications with varying levels of complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2014

Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation

Purpose: Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superior–inferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). Methods: Image quality improvement methods are proposed to mitigate the cone and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases. Results: Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superior–inferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan cases, respectively. In terms of efficiency boost, an overall 3.1 × speedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases. Conclusions: The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

A NEW REDUCTION OF THE BLANCO COSMOLOGY SURVEY: AN OPTICALLY SELECTED GALAXY CLUSTER CATALOG AND A PUBLIC RELEASE OF OPTICAL DATA PRODUCTS

The Blanco Cosmology Survey is a four-band (griz) optical-imaging survey of ∼80 deg{sup 2} of the southern sky. The survey consists of two fields centered approximately at (R.A., decl.) = (23{sup h}, –55°) and (5{sup h}30{sup m}, –53°) with imaging sufficient for the detection of L {sub *} galaxies at redshift z ≤ 1. In this paper, we present our reduction of the survey data and describe a new technique for the separation of stars and galaxies. We search the calibrated source catalogs for galaxy clusters at z ≤ 0.75 by identifying spatial over-densities of red-sequence galaxies and report the coordinates, redshifts, and optical richnesses, λ, for 764 galaxy clusters at z ≤ 0.75. This sample, >85% of which are new discoveries, has a median redshift of z = 0.52 and median richness λ(0.4 L {sub *}) = 16.4. Accompanying this paper we also release full survey data products including reduced images and calibrated source catalogs. These products are available at http://data.rcc.uchicago.edu/dataset/blanco-cosmology-survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

Chae, Junghoon↗
DOE OSTI2015

The 21-SPONGE H i absorption survey. I. Techniques and initial results

We present methods and results from “21 cm Spectral Line Observations of Neutral Gas with the EVLA” (21-SPONGE), a large survey for Galactic neutral hydrogen (H i) absorption with the Karl G. Jansky Very Large Array (VLA). With the upgraded capabilities of the VLA, we reach median rms noise in optical depth of σ{sub τ}=9×10{sup −4} per 0.42 km s{sup −1} channel for the 31 sources presented here. Upon completion, 21-SPONGE will be the largest H i absorption survey with this high sensitivity. We discuss the observations and data reduction strategies, as well as line fitting techniques. We prove that the VLA bandpass (BP) is stable enough to detect broad, shallow lines associated with warm H i, and we show that BP observations can be combined in time to reduce spectral noise. In combination with matching H i emission profiles from the Arecibo Observatory (∼3.′5 angular resolution), we estimate excitation (or spin) temperatures (T{sub s}) and column densities for Gaussian components fitted to sightlines along which we detect H i absorption (30/31). We measure temperatures up to T{sub s}∼1500 K for individual lines, showing that we can probe the thermally unstable interstellar medium (ISM) directly. However, we detect fewer of these thermally unstable components than expected from previous observational studies. We probe a wide range in column density between ∼10{sup 16} and >10{sup 21} cm{sup −2} for individual H i clouds. In addition, we reproduce the trend between cold gas fraction and average T{sub s} found by the Kim et al. synthetic observations of a hydrodynamic ISM simulation. Finally, we compare methods for estimating T{sub s} using H i observations.

79 ASTRONOMY AND ASTROPHYSICS↗