8,064 research outputs found

    The SILCC (SImulating the LifeCycle of molecular Clouds) project: I. Chemical evolution of the supernova-driven ISM

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    The SILCC project (SImulating the Life-Cycle of molecular Clouds) aims at a more self-consistent understanding of the interstellar medium (ISM) on small scales and its link to galaxy evolution. We simulate the evolution of the multi-phase ISM in a 500 pc x 500 pc x 10 kpc region of a galactic disc, with a gas surface density of ΣGAS=10  M⊙/pc2\Sigma_{_{\rm GAS}} = 10 \;{\rm M}_\odot/{\rm pc}^2. The Flash 4.1 simulations include an external potential, self-gravity, magnetic fields, heating and radiative cooling, time-dependent chemistry of H2_2 and CO considering (self-) shielding, and supernova (SN) feedback. We explore SN explosions at different (fixed) rates in high-density regions (peak), in random locations (random), in a combination of both (mixed), or clustered in space and time (clustered). Only random or clustered models with self-gravity (which evolve similarly) are in agreement with observations. Molecular hydrogen forms in dense filaments and clumps and contributes 20% - 40% to the total mass, whereas most of the mass (55% - 75%) is in atomic hydrogen. The ionised gas contributes <10%. For high SN rates (0.5 dex above Kennicutt-Schmidt) as well as for peak and mixed driving the formation of H2_2 is strongly suppressed. Also without self-gravity the H2_2 fraction is significantly lower (∼\sim 5%). Most of the volume is filled with hot gas (∼\sim90% within ±\pm2 kpc). Only for random or clustered driving, a vertically expanding warm component of atomic hydrogen indicates a fountain flow. Magnetic fields have little impact on the final disc structure. However, they affect dense gas (n≳10  cm−3n\gtrsim 10\;{\rm cm}^{-3}) and delay H2_2 formation. We highlight that individual chemical species, in particular atomic hydrogen, populate different ISM phases and cannot be accurately accounted for by simple temperature-/density-based phase cut-offs.Comment: 30 pages, 23 figures, submitted to MNRAS. Comments welcome! For movies of the simulations and download of selected Flash data see the SILCC website: http://www.astro.uni-koeln.de/silc

    Dispersion Relations for Thermally Excited Waves in Plasma Crystals

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    Thermally excited waves in a Plasma crystal were numerically simulated using a Box_Tree code. The code is a Barnes_Hut tree code proven effective in modeling systems composed of large numbers of particles. Interaction between individual particles was assumed to conform to a Yukawa potential. Particle charge, mass, density, Debye length and output data intervals are all adjustable parameters in the code. Employing a Fourier transform on the output data, dispersion relations for both longitudinal and transverse wave modes were determined. These were compared with the dispersion relations obtained from experiment as well as a theory based on a harmonic approximation to the potential. They were found to agree over a range of 0.9<k<5, where k is the shielding parameter, defined by the ratio between interparticle distance a and dust Debye length lD. This is an improvement over experimental data as current experiments can only verify the theory up to k = 1.5.Comment: 8 pages, Presented at COSPAR '0

    Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

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    This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas

    Machine Learning technique for isotopic determination of radioisotopes using HPGe γ\mathrm{\gamma}-ray spectra

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    γ\mathrm{\gamma}-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing γ\mathrm{\gamma}-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application

    Rapid gravity filtration operational performance assessment and diagnosis for preventative maintenance from on-line data

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    Rapid gravity filters, the final particulate barrier in many water treatment systems, are typically monitored using on-line turbidity, flow and head loss instrumentation. Current metrics for assessing filtration performance from on-line turbidity data were critically assessed and observed not to effectively and consistently summarise the important properties of a turbidity distribution and the associated water quality risk. In the absence of a consistent risk function for turbidity in treated water, using on-line turbidity as an indicative rather than a quantitative variable appears to be more practical. Best practice suggests that filtered water turbidity should be maintained below 0.1 NTU, at higher turbidity we can be less confident of an effective particle and pathogen barrier. Based on this simple distinction filtration performance has been described in terms of reliability and resilience by characterising the likelihood, frequency and duration of turbidity spikes greater than 0.1 NTU. This view of filtration performance is then used to frame operational diagnosis of unsatisfactory performance in terms of a machine learning classification problem. Through calculation of operationally relevant predictor variables and application of the Classification and Regression Tree (CART) algorithm the conditions associated with the greatest risk of poor filtration performance can be effectively modelled and communicated in operational terms. This provides a method for an evidence based decision support which can be used to efficiently manage individual pathogen barriers in a multi-barrier system

    Assessment of the probability of contaminating Mars

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    New methodology is proposed to assess the probability that the planet Mars will by biologically contaminated by terrestrial microorganisms aboard a spacecraft. Present NASA methods are based on the Sagan-Coleman formula, which states that the probability of contamination is the product of the expected microbial release and a probability of growth. The proposed new methodology extends the Sagan-Coleman approach to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Three different types of microbial release are distinguished in the model for assessing the probability of contamination. The number of viable microbes released by each mechanism depends on the bio-burden in various locations on the spacecraft and on whether the spacecraft landing is accomplished according to plan. For each of the three release mechanisms a probability of growth is computed, using a model for transport into an environment suited to microbial growth
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