In this article we present a quantitative analysis of the second positive system of molecular nitrogen and the first negative system of the molecular nitrogen cation excited in the presence of ionizing radiation. Optical emission spectra of atmospheric air and nitrogen surrounding 210Po sources were measured from 250 to 400 nm. Multi-Boltzmann and non-Boltzmann vibrational distribution spectral models were used to determine the vibrational temperature and vibrational distribution function of the emitting N2(C3Πu) and N2+(B2Σ+u) states. A zero-dimensional kinetic model, based on the electron energy distribution function (EEDF) and steady-state excitation and de-excitation of N2(X1Σ+g), N2+(B2Σ+u), N2+(X2Σ+g), N4+, O2+, and N2(C3Πu, v), was developed for the prediction of the relative spectral intensity of both the N2+(B2Σ+u → X2Σ+g) emission band and the vibrational bands of N2(C3Πu → B3Πg) for comparison with the experimental data.
The Spectral Physics Environment for Advanced Remote Sensing (SPEARS) application programming interface (API) is a Python-based, line-by-line, local thermal equilibrium (LTE) spectral modeling code which is optimized for simultaneously synthesizing optical spectra from any combination of fundamental spectroscopic databases. In this article, we contribute two novel spectral modeling techniques to the scientific literature. First we describe how SPEARS integrates a physics-based collisional model for calculating pressure broadening in the absence of available broadening coefficients. With this collisional model implementation, a generalized approach to fundamental spectroscopic databases can be achieved across multiple databases. We also detail our adaptive grid mesh algorithm developed to make the code scalable for simulating large spectral bandwidths at high spectral fidelity using intuitive grid parameters. We present comparisons to other modeling tools, experiments, and provide a discussion on the SPEARS user interface.
We report the details of an infrared, laser absorption diagnostic capable of quantifying aluminum monoxide temperature and column density at 100 kHz repetition rate. This novel technique employs a near infrared MEMS-VCSEL to measure rotationally resolved optical absorption spectra of aluminum monoxide $A^2\Pi_i$ - $X^2\Sigma^+$ from approximately 7400 –7900 cm-1. Temperatures and column densities are extracted from model regressions to provide temporally resolved thermochemical information on aluminum oxidation reactions. The measurement capability is demonstrated by performing 100 kHz measurement in the plume of an exploding bridgewire with measured temperatures of 3450–3100 K and column densities of 1– 11 x 1016cm -2. To the authors knowledge, this is the first use of the AlO $A^2\Pi_i$ - $X^2\Sigma^+$ transition to characterize aluminum combustion environments. Details regarding signal extraction and calibration of MEMS-VCSEL spectra are also included. Although unsuccessful, efforts to extract kinetic temperature and column density from simultaneously measured, atomic aluminum 2P3/2,1/2-2S1/2 transitions at 7618 cm-1 and 7602 cm-1 are also described.
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.
Two surface chemical explosive tests were observed for the Large Surface Explosion Coupling Experiment (LSECE) at the Nevada National Security Site in October 2020. The tests consisted of two one-ton explosions, one occurring before dawn and one occurring mid- afternoon. LSECE was performed in the same location as previous underground tests and aimed to explore the relationship between surface and underground explosions in support of global nonproliferation efforts. Several pieces of remote sensing equipment were deployed from a trailer 2.02 km from ground zero including high-speed cameras, radiometers and a spectrometer. The data collected from these tests will increase the knowledge of large surface chemical explosive signatures.
Detonation of explosive devices produces extremely hazardous fragments and hot, luminous fireballs. Prior experimental investigations of these post-detonation environments have primarily considered devices containing hundreds of grams of explosives. While relevant to many applications, such large- scale testing also significantly restricts experimental diagnostics and provides limited data for model validation. As an alternative, the current work proposes experiments and simulations of the fragmentation and fireballs from commercial detonators with less than a gram of high explosive. As demonstrated here, reduced experimental hazards and increased optical access significantly expand the viability of advanced imaging and laser diagnostics. Notable developments include the first known validation of MHz-rate optical fragment tracking and the first ever Coherent Anti-Stokes Raman Scattering (CARS) measures of post-detonation fireball temperatures. While certainly not replacing the need for full-scale verification testing, this work demonstrates new opportunities to accelerate developments of diagnostics and predictive models of post-detonation environments.
We report isotopic shifts and hyperfine structures of selected U transitions employing tunable spectroscopy viz: laser-induced fluorescence and laser absorption spectroscopy of laser ablation plumes. The plasmas were produced during ns laser ablation on a natural U metal target which contains 0.73% 235U. Our results show that isotopic shifts between 238U and 235U are entangled with hyperfine structures of 235U. Measurements obtained using laser-induced fluorescence are affected by the high absorbance of 238U. Time-resolved laser absorption spectroscopy is carried out for evaluating the optical absorption and estimating the hyperfine constants.