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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Simulating Microbial Community Patterning Using Biocellion

Mathematical modeling and computer simulation are important tools for understanding complex interactions between cells and their biotic and abiotic environment: similarities and differences between modeled and observed behavior provide the basis for hypothesis forma- tion. Momeni et al. [5] investigated pattern formation in communities of yeast strains engaging in different types of ecological interactions, comparing the predictions of mathematical modeling and simulation to actual patterns observed in wet-lab experiments. However, simu- lations of millions of cells in a three-dimensional community are ex- tremely time-consuming. One simulation run in MATLAB may take a week or longer, inhibiting exploration of the vast space of parameter combinations and assumptions. Improving the speed, scale, and accu- racy of such simulations facilitates hypothesis formation and expedites discovery. Biocellion is a high performance software framework for ac- celerating discrete agent-based simulation of biological systems with millions to trillions of cells. Simulations of comparable scale and accu- racy to those taking a week of computer time using MATLAB require just hours using Biocellion on a multicore workstation. Biocellion fur- ther accelerates large scale, high resolution simulations using cluster computers by partitioning the work to run on multiple compute nodes. Biocellion targets computational biologists who have mathematical modeling backgrounds and basic C++ programming skills. This chap- ter describes the necessary steps to adapt the original Momeni et al.'s model to the Biocellion framework as a case study.

metabolic interaction↗
DOE OSTI2013

Texture analysis improves level set segmentation of the anterior abdominal wall

Purpose: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention.Methods: In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors’ approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.Results: Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2–5 mm) for methods that did not use the texture.Conclusions: The authors’ approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

DENSE GAS FRACTION AND STAR FORMATION EFFICIENCY VARIATIONS IN THE ANTENNAE GALAXIES

We use the Combined Array for Research in Millimeter-wave Astronomy (CARMA) millimeter interferometer to map the Antennae Galaxies (NGC 4038/39), tracing the bulk of the molecular gas via the {sup 12}CO(1–0) line and denser molecular gas via the high density transitions HCN(1–0), HCO{sup +}(1–0), CS(2–1), and HNC(1–0). We detect bright emission from all tracers in both the two nuclei and three locales in the overlap region between the two nuclei. These three overlap region peaks correspond to previously identified “supergiant molecular clouds.” We combine the CARMA data with Herschel infrared (IR) data to compare observational indicators of the star formation efficiency (star formation rate/H{sub 2} ∝ IR/CO), dense gas fraction (HCN/CO), and dense gas star formation efficiency (IR/HCN). Regions within the Antennae show ratios consistent with those seen for entire galaxies, but these ratios vary by up to a factor of six within the galaxy. The five detected regions vary strongly in both their integrated intensities and these ratios. The northern nucleus is the brightest region in millimeter-wave line emission, while the overlap region is the brightest part of the system in the IR. We combine the CARMA and Herschel data with ALMA CO data to report line ratio patterns for each bright point. CO shows a declining spectral line energy distribution, consistent with previous studies. HCO{sup +} (1–0) emission is stronger than HCN (1–0) emission, perhaps indicating either more gas at moderate densities or higher optical depth than is commonly seen in more advanced mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Vegetation heterogeneity reflects soil thermal state and surface soil displacement in a thawing permafrost landscape

Thawing permafrost has the potential to dramatically alter the physical and ecological structure of northern landscapes. Warming of the Arctic and subsequent degradation of permafrost have created a need to assess the stability and movement of soils on hillslopes and the potential impacts on ecosystem structure. In this work, we explore the relationships among vegetation heterogeneity, soil temperature, and soil surface displacements observed from 2019 to 2022 in a watershed in the discontinuous permafrost region on the Seward Peninsula of Alaska. Vegetation heterogeneity was measured as the standard deviation (SD) of the normalized difference vegetation index (NDVI) from 3 m PlanetScope satellite imagery around each soil temperature and active layer thickness observation. Locations of observations were clustered into three soil thermal groups, warm, intermediate, and cold, based on soil temperature and active layer thickness. Average annual horizontal surface displacements were significantly lower for soils within the warm thermal group (median = 0.033 m yr −1 ) compared to soils within the cold thermal group (median = 0.090 m yr −1 ; p < 0.001). Conversely, vegetation heterogeneity was significantly higher in the warm (median = 0.014 SD NDVI; p = 0.002) and intermediate (median = 0.015 SD NDVI; p = 0.002) groups compared with the cold thermal group (median = 0.012 SD NDVI), suggesting a warming-induced shift in vegetation community complexity. Because of the observed associations of ground surface displacement rates and vegetation heterogeneity with soil thermal state, we hypothesize that warming soil conditions induce changes in the rates and patterns of hillslope erosion due to an increase in surface movement as near-surface permafrost thaws, followed by a decrease as the permafrost table deepens and excess ice content diminishes. The transition to warm soils promotes surface ecosystem transformation, shifting the dominant vegetation at the site, given the warming climatic conditions of the region. We integrated our observations of soil temperature, vegetation heterogeneity, and soil surface displacements into a conceptual model that describes the co-evolution of hillslopes and vegetation in warming permafrost environments, which is currently unrepresented in earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Infrared nanospectroscopy characterization of metal oxide photoresists

Implementation of extreme ultraviolet (EUV) lithography in high-volume semiconductor manufacturing requires a reliable and scalable EUV resist platform. A mechanistic understanding of the pros and cons of different EUV resist materials is critically important. However, most material characterization methods with nanometer resolution use an x-ray photon or electron beam as the probe, which often cause damage to the photoresist film during measurement. Here, we illustrated the use of non-destructive infrared nanospectroscopy [or nano-Fourier-Transform infrared spectroscopy (nano-FTIR)] to obtain spatially resolved composition information in patterned photoresist films. Further, clear evidence of exposure-induced chemical modification was observed at a spatial resolution down to 40 nm, well below the diffraction limit of infrared light. With improvements, such a nano-FTIR technique with nanoscale spatial resolution, chemical sensitivity, and minimal radiation damage can be a promising candidate for the fundamental study of material properties relevant to EUV lithography.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Quality of life trajectories during the first three years after diagnosis of breast cancer: the NEON-BC study

Abstract Background We aimed to identify and characterize quality of life trajectories up to 3 years after breast cancer diagnosis. Methods A total of 460 patients were evaluated at baseline (before treatments), and after 1- and 3-years. Patient-reported outcomes, including quality of life (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30, QLQ-C30), anxiety, depression and sleep quality, were assessed in all evaluations. Model-based clustering was used to identify quality of life trajectories. Results We identified four trajectories without intersection during 3 years. The two trajectories characterized by better quality of life depicted relatively stable scores; in the other trajectories, quality of life worsened until 1 year, though in one of them the score at 3 years improved. Sociodemographic and clinical characteristics at baseline did not differ between trajectories, except for mastectomy, which was higher in the worst trajectory. Anxiety, depression and poor sleep quality increased from the best to the worst trajectory. Conclusions The type of surgery and the variation of other patient-reported outcomes were associated with the course of quality of life over 3 years. More research to understand the heterogeneity of individual trajectories within these major patterns of variation is needed.

Lopes-Conceição, Luisa↗
DOE OSTI2016

Fault Tolerant Frequent Pattern Mining

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing, though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.

Shohdy, Sameh↗
DOE OSTI2021

A Meta-Analysis of the Protein Components in Rattlesnake Venom

The specificity and potency of venom components give them a unique advantage in developing various pharmaceutical drugs. Though venom is a cocktail of proteins, rarely are the synergy and association between various venom components studied. Understanding the relationship between various components of venom is critical in medical research. Using meta-analysis, we observed underlying patterns and associations in the appearance of the toxin families. For Crotalus, Dis has the most associations with the following toxins: PDE; BPP; CRL; CRiSP; LAAO; SVMP P-I and LAAO; SVMP P-III and LAAO. In Sistrurus venom, CTL and NGF have the most associations. These associations can predict the presence of proteins in novel venom and understand synergies between venom components for enhanced bioactivity. Using this approach, the need to revisit the classification of proteins as major components or minor components is highlighted. The revised classification of venom components is based on ubiquity, bioactivity, the number of associations, and synergies. The revised classification can be expected to trigger increased research on venom components, such as NGF, which have high biomedical significance. Using hierarchical clustering, we observed that the genera’s venom compositions were similar, based on functional characteristics rather than phylogenetic relationships.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Prospects for chemically tagging stars in the galaxy

It is now well-established that the elemental abundance patterns of stars hold key clues not only to their formation, but also to the assembly histories of galaxies. One of the most exciting possibilities is the use of stellar abundance patterns as “chemical tags” to identify stars that were born in the same molecular cloud. In this paper, we assess the prospects of chemical tagging as a function of several key underlying parameters. We show that in the fiducial case of 10{sup 4} distinct cells in chemical space and 10{sup 5}−10{sup 6} stars in the survey, one can expect to detect ∼10{sup 2}−10{sup 3} groups that are ⩾5σ overdensities in the chemical space. However, we find that even very large overdensities in chemical space do not guarantee that the overdensity is due to a single set of stars from a common birth cloud. In fact, for our fiducial model parameters, the typical 5σ overdensity is comprised of stars from a wide range of clusters with the most dominant cluster contributing only 25% of the stars. The most important factors limiting the identification of disrupted clusters via chemical tagging are the number of chemical cells in the chemical space and the survey sampling rate of the underlying stellar population. Both of these factors can be improved through strategic observational plans. While recovering individual clusters through chemical tagging may prove challenging, we show, in agreement with previous work, that different CMFs imprint different degrees of clumpiness in chemical space. These differences provide the opportunity to statistically reconstruct the slope and high-mass cutoff of CMF and its evolution through cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Hoover, Richard B.↗
DOE OSTI2016

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Summary Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.

Solaimani, Mohiuddin↗
DOE OSTI2015

Comparison of the local binding motifs in the imidazolium-based ionic liquids [EMIM][BF{sub 4}] and [EMMIM][BF{sub 4}] through cryogenic ion vibrational predissociation spectroscopy: Unraveling the roles of anharmonicity and intermolecular interactions

We clarify the role of the critical imidazolium C{sub (2)}H position (the central C between N atoms in the heterocycle) in the assembly motif of the [EMIM][BF{sub 4}] ionic liquid by analyzing the vibrational spectra of the bare EMIM{sup +} ion as well as that of the cationic [EMIM]{sub 2}[BF{sub 4}]{sup +} (EMIM{sup +} = 1-ethyl-3-methylimidazolium, C{sub 6}H{sub 11}N{sub 2}{sup +}) cluster. Vibrational spectra of the cold, mass-selected ions are obtained using cryogenic ion vibrational predissociation of weakly bound D{sub 2} molecules formed in a 10 K ion trap. The C{sub (2)}H behavior is isolated by following the evolution of key vibrational features when the C{sub (2)} hydrogen, the proposed binding location of the anion to the imidazolium ring, is replaced by either deuterium or a methyl group (i.e., in the EMMIM{sup +} analogue). Strong features in the ring CH stretching region of the bare ion are traced to Fermi resonances with overtones of lower frequency modes. Upon incorporation into the EMIM{sup +} ⋅ ⋅ ⋅ BF{sub 4}{sup −} ⋅ ⋅ ⋅ EMIM{sup +} ternary complex, the C{sub (2)}H oscillator strength is dramatically increased, accounting for the much more complicated patterns derived from the EMIM{sup +} ring CH stretches in the light isotopomer, which are strongly suppressed in the deuterated analogue. Further changes in the spectra that occur when the C{sub (2)}H is replaced by a methyl group are consistent with BF{sub 4}{sup −} attachment directly to the imidazolium ring in an arrangement that maximizes the electrostatic interaction between the molecular ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.

Clustering↗
DOE OSTI2015

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver on pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

Spacecraft Parachute Recovery System Testing from a Failure Rate Perspective

Spacecraft parachute recovery systems, especially those with a parachute cluster, require testing to identify and reduce failures. This is especially important when the spacecraft in question is human-rated. Due to the recent effort to make spaceflight affordable, the importance of determining a minimum requirement for testing has increased. The number of tests required to achieve a mature design, with a relatively constant failure rate, can be estimated from a review of previous complex spacecraft recovery systems. Examination of the Apollo parachute testing and the Shuttle Solid Rocket Booster recovery chute system operation will clarify at which point in those programs the system reached maturity. This examination will also clarify the risks inherent in not performing a sufficient number of tests prior to operation with humans on-board. When looking at complex parachute systems used in spaceflight landing systems, a pattern begins to emerge regarding the need for a minimum amount of testing required to wring out the failure modes and reduce the failure rate of the parachute system to an acceptable level for human spaceflight. Not only a sufficient number of system level testing, but also the ability to update the design as failure modes are found is required to drive the failure rate of the system down to an acceptable level. In addition, sufficient data and images are necessary to identify incipient failure modes or to identify failure causes when a system failure occurs. In order to demonstrate the need for sufficient system level testing prior to an acceptable failure rate, the Apollo Earth Landing System (ELS) test program and the Shuttle Solid Rocket Booster Recovery System failure history will be examined, as well as some experiences in the Orion Capsule Parachute Assembly System will be noted.

Stewart, Christine E.↗
DOE OSTI2022

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2006

Nucleosynthesis: Stellar and Solar Abundances and Atomic Data

Abundance observations indicate the presence of often surprisingly large amounts of neutron capture (i.e., s- and r-process) elements in old Galactic halo and globular cluster stars. These observations provide insight into the nature of the earliest generations of stars in the Galaxy the progenitors of the halo stars responsible for neutron-capture synthesis. Comparisons of abundance trends can be used to understand the chemical evolution of the Galaxy and the nature of heavy element nucleosynthesis. In addition age determinations, based upon long-lived radioactive nuclei abundances, can now be obtained. These stellar abundance determinations depend critically upon atomic data. Improved laboratory transition probabilities have been recently obtained for a number of elements. These new gf values have been used to greatly refine the abundances of neutron-capture elemental abundances in the solar photosphere and in very metal-poor Galactic halo stars. The newly determined stellar abundances are surprisingly consistent with a (relative) Solar System r-process pattern, and are also consistent with abundance predictions expected from such neutron-capture nucleosynthesis.

Cowan, John J.↗
NASA NTRS2008

Experimental Measurement of RCS Jet Interaction Effects on a Capsule Entry Vehicle

An investigation was made in NASA Langley Research Center s 31-Inch Mach 10 Tunnel to determine the effects of reaction-control system (RCS) jet interactions on the aft-body of a capsule entry vehicle. The test focused on demonstrating and improving advanced measurement techniques that would aid in the rapid measurement and visualization of jet interaction effects for the Orion Crew Exploration Vehicle while providing data useful for developing engineering models or validation of computational tools used to assess actual flight environments. Measurements included global surface imaging with pressure and temperature sensitive paints and three-dimensional flow visualization with a scanning planar laser induced fluorescence technique. The wind tunnel model was fabricated with interchangeable parts for two different aft-body configurations. The first, an Apollo-like configuration, was used to focus primarily on the forward facing roll and yaw jet interactions which are known to have significant aft-body heating augmentation. The second, an early Orion Crew Module configuration (4-cluster jets), was tested blowing only out of the most windward yaw jet, which was expected to have the maximum heating augmentation for that configuration. Jet chamber pressures and tunnel flow conditions were chosen to approximate early Apollo wind tunnel test conditions. Maximum heating augmentation values measured for the Apollo-like configuration (>10 for forward facing roll jet and 4 for yaw jet) using temperature sensitive paint were shown to be similar to earlier experimental results (Jones and Hunt, 1965) using a phase change paint technique, but were acquired with much higher surface resolution. Heating results for the windward yaw jet on the Orion configuration had similar augmentation levels, but affected much less surface area. Numerical modeling for the Apollo-like yaw jet configuration with laminar flow and uniform jet outflow conditions showed similar heating patterns, qualitatively, but also showed significant variation with jet exit divergence angle, with as much as 25 percent variation in heat flux intensity for a 10 degree divergence angle versus parallel outflow. These results along with the fabrication methods and advanced measurement techniques developed will be used in the next phase of testing and evaluation for the updated Orion RCS configuration.

Buck, Gregory M.↗