Search

This study characterized uranium metal dust cloud combustion using absorption spectroscopy, imaging, and broadband emission measurements. Other metals were similarly combusted to establish correlations between results from this study and those found in the literature. It was determined that the burn temperature of uranium was limited to the volatilization temperature of uranium dioxide. Combustion behavior was similar to that of other refractory metals in terms of burn time and the observation of exploding particle behavior. [ABSTRACT FROM AUTHOR]

The reactivity of titanium and boron mixtures under detonation initiation in air is examined experimentally in a constant volume blast chamber. Fine powder mixtures and mechanical alloys are pressed into compacts and are ignited using an HMX-based explosive initiated with both single point and triple point detonator configurations. Transient pressure measurements, optical imaging, pyrometry, and spectroscopy are performed to analyse the reaction. All mixtures show no significant enhancement in the primary blast wave strength, indicating a relatively slow reaction. However, measurable increases in overpressure are generated due to Ti and/or B reaction. It is found that Ti/2B mechanical alloys significantly outperform blended powder mixtures in generating larger overpressures, yielding energy releases of 45 % and 20 %, respectively. Triple point initiation of the mechanical alloys further enhances the overpressure generation when compared to single point initiation of the alloys, increasing the energy yield. The overpressure of blended powders is also exceeded by a TiB2 compound, suggesting that the intermetallic reaction may be less critical than previously thought. Detonation merging at the plane of interaction between explosive and Ti/2B material is shown to significantly enhance conversion. Spectroscopic measurements show the appearance of BO2 emission relatively late after detonation in the most reactive Ti/2B reactive mechanical alloys, and it appears strongest in system that show greatest reactivity. [ABSTRACT FROM AUTHOR]

Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline. [ABSTRACT FROM AUTHOR]