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At least 37 records · Page 2

Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

Slaba, T. C.

Comparison of Space Radiation GCR Models to Recent AMS Data

This paper is the third in a series of comparisons of American (NASA) and Russian (ROSCOSMOS) space radiation calculations. The present work focuses on calculation of fluxes of galactic cosmic rays (GCR), which are a constant source of radiation that constitutes one of the major hazards during deep space exploration missions for both astronauts/cosmonauts and hardware. In this work, commonly used GCR models are compared with recently published measurements of cosmic ray Hydrogen, Helium, and the Boron-to-Carbon ratio from the Alpha Magnetic Spectrometer (AMS). All of the models were developed and calibrated prior to the publication of the AMS data, therefore this an opportunity to validate the models against an independent data set.

John W. Norbury

AMS Joint/Bilateral Surveys

A historical presentation depicting the International Joint Aerial Surveys AMS has co-created and collaborative efforts that were implemented as result of those efforts.

61 RADIATION PROTECTION AND DOSIMETRY

AmeriFlux US-AMS Argonne Testbed for Multiscale Observational Science (ATMOS)

This is the AmeriFlux version of the carbon flux data for the site US-AMS Argonne Testbed for Multiscale Observational Science (ATMOS). Site Description - This tower is located at Argonne National Laboratory approximately 2 km north of the Des Plaines river and 115 m NNW from the ATMOS meteorological tower. It is downwind of a seasonally wet grassland bordered by forest 40 m N of the flux tower. The area was managed by controlled burns until 2019.

McNicol, Gavin [University of Illinois at Chicago]

American Meteorological Society (AMS) - The Modern Era Retrospective-Analysis for Research and Applications (MERRA) Data and Accessibility

The AM Short Course on The Modern Era Retrospective-analysis for Research and Applications (MERRA) data and accessibility will be held on January 11, 2009 preceding the 89th Annual Meeting in Phoenix, Arizona. Preliminary programs, registration, hotel, and general information will be posted on the AMS Web site in mid-September 2008. Retrospective-analyses (or reanalyses) have been established as an important tool in weather and climate research over the last decade. As computer power increases, the data assimilation and modeling systems improve and become more advanced, the input data quality increases and so reanalyses become more reliable. In 2008, NASA Global Modeling and Assimilation Office began producing a new reanalysis called the Modem Era Retrospective-analysis for Research and Applications (MERRA). The initial data from the reanalysis has been made available to the community and should be complete through 30 years (1979-present) by Fall of 2009. MERRA has taken advantage of the advancement of computing resources to provide users more data than previously available. The native spatial resolution is nominally 1/2 degrees and the surface two dimensional data are one hourly frequency. In addition to the meteorological analysis data, complete mass, energy and momentum budget data and also stratospheric data are provided. The eventual data holdings will exceed 150Tb. In order to facilitate user accessibility to the data, it will be stored in online hard drives (not tape storage) and available through several portals. Subsetting tools will also be available to allow users to tailor their data requests. The goals of this short course are to provide hands on users of reanalyses instruction on MERRA systems and also interactive experience with the online data and access tools. The course is intended for students and research scientists who will be actively interested in accessing and applying MERRA data in their weather, climate or applications work. The course has three parts. There will be an overview of the MERRA system, the validation of the system and the native data format. Second, Instructors will provide examples of weather and climate data analysis using various software packages (primarily GrADS) as well as the online access tools for subsetting and download, as well as visualization (e.g. Giovanni and Google Earth). This will also include examples on changing the data format to fit user's preferences and also to regrid the data for comparisons to other reanalyses and observational data. Lastly, there will he time set aside for participants to have hands on access to the data and software while interacting with the instructors and other developers. The course convener is Dr. Michael Bosilovich, NASA GSFC Global Modeling and Assimilation Office (GMAO). He will be joined by several GMAO, Goddard Earth Science Data and information Services Center (GES DISC) and Software Integration and Visualization Office (SIVO) staff.

daSilva, Arlindo

Very High Cycle Fatigue Testing of AMS 6308 Steel

Lightweight and reliable gearboxes are required for helicopters and future electrical vertical take-off and landing aircraft. Mechanical gears in these applications experience more than 10 7 million fatigue cycles over their operating life and between maintenance intervals. Fatigue data for gear steels above 10 7 cycles is rare due to the testing time required to reach these cycle counts with traditional methods. In this paper, ultrasonic fatigue testing is used to collect mechanical fatigue data on AMS 6308 steel in the 10 7 to 1010 cycle regime.

Thomas Tallerico

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model

Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These properties can be useful for defining relevant signatures for probabilistically identifying the different types of events that occurred, based on the soot that is produced from these events. However, it is rare to observe samples of soot from a detonation or combustion that are not contaminated by outside particles. In this paper, we present a method for resolving mixtures of soot to determine the contributions of sources that may be present in samples of recovered soot. We use Latent Dirichlet Allocation to describe the generative process for a sample of recovered soot, and use Variational Bayesian Inference to learn about the parameters associated with the generative model. We demonstrate the utility of this method by considering real samples of mixtures of soot under various frameworks to show that the model is able to identify the different components present in a sample of soot as well as their mixing proportions.

54 ENVIRONMENTAL SCIENCES