Chemical homogeneity of wide binary system: An approach from Near-Infrared spectroscopy

Wide binaries, with separations between two stars from a few AU to more than several thousand AU, are valuable objects for various research topics in Galactic astronomy. As the number of newly reported wide binaries continues to increase, studying the chemical abundances of their component stars becomes more important. We conducted high-resolution near-infrared (NIR) spectroscopy for six pairs of wide binary candidates using the Immersion Grating Infrared Spectrometer (IGRINS) at the Gemini-South telescope. One pair was excluded from the wide binary samples due to a significant difference in radial velocity between its component stars, while the remaining five pairs exhibited homogeneous properties in 3D motion and chemical composition among the pair stars. The differences in [Fe/H] ranged from 0.00 to 0.07 dex for these wide binary pairs. The abundance differences between components are comparable to the previous results from optical spectroscopy for other samples. In addition, when combining our data with literature data, it appears that the variation of abundance differences increases in wide binaries with larger separations. However, the SVO2324 and SVO3206 showed minimal differences in most elements despite their large separation, supporting the concept of multiple formation mechanisms depending on each wide binary. This study is the first approach to the chemical properties of wide binaries based on NIR spectroscopy. Our results further highlight that NIR spectroscopy is an effective tool for stellar chemical studies based on equivalent measurements of chemical abundances from the two stars in each wide binary system.Comment: 16 pages, 9 figures, accepted for publication in A

Probabilistic Integrated Urban Inundation Modeling Using Sequential Data Assimilation

Urban inundation due to climate change and heavy rainfall is one of the most common natural disasters worldwide. However, it is still insufficient to obtain accurate urban inundation predictions due to various uncertainties coming from input forcing data, model parameters, and observations. Despite of numerous sophisticated data assimilation algorithms proposed to increase the certainty of predictions, there have been few attempts to combine data assimilation with integrated inundation models due to expensive computations and computational instability such as breach of conservation and momentum equations in the updating procedure. In this study, we propose a probabilistic integrated urban inundation modeling scheme using sequential data assimilation. The original integrated urban inundation model consists of a 2D inundation model on the ground surface and a 1D network model of sewer pipes, which are combined by a sub-model to exchange storm water between the ground surface and the sewerage system. In our method, uncertainties of modeling conditions are explicitly expressed by ensembles having different rainfall input, initial conditions, and model parameters. Then, particle filtering(PF), one of sequential data assimilation techniques for non-linear and non-Gaussian models, is applied to sequentially update model states and parameters when new observations are arrived from monitoring systems. Several synthetic experiments are implemented to demonstrate applicability of the proposed method in an urbanized area located in Osaka, Japan. The discussion will be focused on noise specification and updating methods in PF and comparison of accuracy between deterministic and probabilistic inundation modeling methods

Desenvolvimento de um modelo de inundação bidimensional acelerado por GPGPU

Inundações causam enormes prejuízos econômicos e afetam a vida de milhares de pessoas. Elaborar medidas para mitigar os efeitos das inundações é uma tarefa que exige o uso de modelos que simulem com precisão e rapidez o processo de inundação. Diante disso, os objetivos deste trabalho foram: (i) desenvolver uma implementação paralela de um modelo de inundação bidimensional para ser executado em unidades de processamento gráfico de propósito geral (GPGPU) e (ii) determinar o ganho de desempenho em comparação com uma versão sequencial equivalente. Como estudo de caso, fez-se a simulação da inundação do Campus Trindade da bacia hidrográfica da Universidade Federal de Santa Catarina. A versão paralela do modelo foi desenvolvida utilizando linguagem de programação CUDA C e uma estrutura baseada numa versão sequencial do modelo de inundação implementada em linguagem FORTRAN. Este modelo utiliza uma formulação 2D das equações de águas rasas discretizada pelo método de diferenças finitas. Para o desenvolvimento do código computacional utilizou-se o software Visual Studio Community 2013 e CUDA toolkit 8. As simulações foram realizadas em um computador equipado com processador Intel® CoreTM i7-7700L 4.2GHz e GPU GeForce GTX 1060 6GB. Por meio das comparações entre os tempos de simulação verificamos que o modelo paralelo processado em GPGPU foi 70 vezes mais rápido que a versão sequencial executada na CPU, reduzindo o tempo de simulação de 12 horas para 10 minutos. Além disso, os resultados permitiram verificar a evolução do processo de inundação na bacia demonstrando que o uso de GPGPU é uma alternativa promissora na construção de modelos de inundação, para a previsão de cheias e emissão de alerta. Palavras chave: modelo de inundação, GPGPU, CUDAFil: Carlotto, Tomas. Universidad do Estado de Santa Catarina (Brasil).Fil: Innocente, Camyla. Universidad do Estado de Santa Catarina (Brasil).Fil: Lee, Seungsoo. Universidad do Estado de Santa Catarina (Brasil).Fil: Chaffe, Pedro. Universidad do Estado de Santa Catarina (Brasil)