Site Reliability Engineers (SREs) play a key role in issue identification and resolution. After an issue is reported, SREs come together in a virtual room (collaboration platform) to triage the issue. While doing so, they leave behind a wealth of inf
ormation which can be used later for triaging similar issues. However, usability of the conversations offer challenges due to them being i) noisy and ii) unlabelled. This paper presents a novel approach for issue artefact extraction from the noisy conversations with minimal labelled data. We propose a combination of unsupervised and supervised model with minimum human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pretrained language model for artefact prediction on a large amount of conversation data. Experimental results on our dataset show that the proposed ensemble of unsupervised and supervised model is better than using either one of them individually.
We present the time-dependent properties of a poorly known OH/IR star $-$ IRAS 18278+0931 (hereafter, IRAS 18+09) towards the Ophiuchus constellation. We have carried out long-term optical/near-infrared (NIR) photometric and spectroscopic observation
s to study the object. From optical $R$- and $I$-band light curves, the period of IRAS 18+09 is estimated to be 575 $pm$ 30 days and the variability amplitudes range from $Delta$R $sim$ 4.0 mag to $Delta$I $sim$ 3.5 mag. From the standard Period-Luminosity (PL) relations, the distance ($D$) to the object, 4.0 $pm$ 1.3 kpc, is estimated. Applying this distance in the radiative transfer model, the spectral energy distribution (SED) are constructed from multi-wavelength photometric and IRAS-LRS spectral data which provides the luminosity, optical depth, and gas mass-loss rate (MLR) of the object to be 9600 $pm$ 500 $L_{odot}$, 9.1 $pm$ 0.6 at 0.55 $mu$m and 1.0$times$10$^{-6}$ M$_odot$ yr$^{-1}$, respectively. The current mass of the object infers in the range 1.0 $-$ 1.5 $M_odot$ assuming solar metallicity. Notably, the temporal variation of atomic and molecular features (e.g., TiO, Na I, Ca I, CO, H$_2$O) over the pulsation cycle of the OH/IR star illustrates the sensitivity of the spectral features to the dynamical atmosphere as observed in pulsating AGB stars.
We present here quantitative diagnostic tools for cool giants that employ low-resolution near-infrared spectroscopy in the $K$-band for stellar population studies. In this study, a total of 260 cool giants (177 stars observed with X-shooter and 83 st
ars observed with NIFS) are used covering a wider metallicity range than in earlier works. We measure equivalent widths of some of the selected important $K$-band spectral features like Na I, Fe I, and $^{12}$CO after degrading the spectral resolution (R $sim$ 1200) to investigate the spectral behavior with fundamental parameters (e.g. effective temperature and metallicity). We derive empirical relations to measure effective temperature using the $^{12}$CO first-overtone band at 2.29 $mu$m and 2.32 $mu$m and show a detailed quantitative metallicity dependence of these correlations. We find that the empirical relations based on solar-neighborhood stars can incorporate large uncertainty in evaluating $T_{eff}$ for metal-poor or metal-rich stars. Furthermore, we explore all the spectral lines to establish the empirical relation with metallicity and find that the quadratic fit of the combination of Na I and $^{12}$CO at 2.29 $mu$m lines yields a reliable empirical relation at [$Fe/H$] $leq$ $-$0.4 dex, while a linear fit of any line offers a good metallicity scale for stars having [$Fe/H$] $geq$ 0.0 dex.
We develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials sci
ence, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our simulation approach consists of a finite element spatial discretization of the fully-coupled nonlinear system of partial differential equations at the microscale, which is treated implicitly in time with a preconditioned Jacobian-free Newton-Krylov method. The approach allows time steps larger than those restricted by the traditional explicit CFL limit and is algorithmically scalable as well as efficient due to an effective preconditioning strategy based on algebraic multigrid and block factorization. We implement this approach in the open-source Tusas framework, which is a general, flexible tool developed in C++ for solving coupled systems of nonlinear partial differential equations. The performance of our approach is analyzed in terms of algorithmic scalability and efficiency, while the computational performance of Tusas is presented in terms of parallel scalability and efficiency on emerging heterogeneous architectures. We demonstrate that modern algorithms, discretizations, and computational science, and heterogeneous hardware provide a robust route for predictive phase-field simulation of microstructure evolution during additive manufacturing.
Quality control in additive manufacturing can be achieved through variation control of the quantity of interest (QoI). We choose in this work the microstructural microsegregation to be our QoI. Microsegregation results from the spatial redistribution
of a solute element across the solid-liquid interface that forms during solidification of an alloy melt pool during the laser powder bed fusion process. Since the process as well as the alloy parameters contribute to the statistical variation in microstructural features, uncertainty analysis of the QoI is essential. High-throughput phase-field simulations estimate the solid-liquid interfaces that grow for the melt pool solidification conditions that were estimated from finite element simulations. Microsegregation was determined from the simulated interfaces for different process and alloy parameters. Correlation, regression, and surrogate model analyses were used to quantify the contribution of different sources of uncertainty to the QoI variability. We found negligible contributions of thermal gradient and Gibbs-Thomson coefficient and considerable contributions of solidification velocity, liquid diffusivity, and segregation coefficient on the QoI. Cumulative distribution functions and probability density functions were used to analyze the distribution of the QoI during solidification. Our approach, for the first time, identifies the uncertainty sources and frequency densities of the QoI in the solidification regime relevant to additive manufacturing.
Numerical simulations are used in this work to investigate aspects of microstructure and microsegregation during rapid solidification of a Ni-based superalloy in a laser powder bed fusion additive manufacturing process. Thermal modeling by finite ele
ment analysis simulates the laser melt pool, with surface temperatures in agreement with in situ thermographic measurements on Inconel 625. Geometric and thermal features of the simulated melt pools are extracted and used in subsequent mesoscale simulations. Solidification in the melt pool is simulated on two length scales. For the multicomponent alloy Inconel 625, microsegregation between dendrite arms is calculated using the Scheil-Gulliver solidification model and DICTRA software. Phase-field simulations, using Ni-Nb as a binary analogue to Inconel 625, produced microstructures with primary cellular/dendritic arm spacings in agreement with measured spacings in experimentally observed microstructures and a lesser extent of microsegregation than predicted by DICTRA simulations. The composition profiles are used to compare thermodynamic driving forces for nucleation against experimentally observed precipitates identified by electron and X-ray diffraction analyses. Our analysis lists the precipitates that may form from FCC phase of enriched interdendritic compositions and compares these against experimentally observed phases from 1 h heat treatments at two temperatures: stress relief at 1143 K (870{deg}C) or homogenization at 1423 K (1150{deg}C).
