Zobrazeno 1 - 10
of 15 888
pro vyhledávání: '"A. Cuéllar"'
Autor:
Cuéllar, Antonio, Amico, Enrico, Serpieri, Jacopo, Cafiero, Gioacchino, Baars, Woutijn J, Discetti, Stefano, Ianiro, Andrea
We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement tec
Externí odkaz:
http://arxiv.org/abs/2410.12778
Autor:
Lopez, S., Afruni, A., Zamora, D., Tejos, N., Ledoux, C., Hernandez, J., Berg, T. A. M., Cortes, H., Urbina, F., Johnston, E. J., Barrientos, L. F., Bayliss, M. B., Cuellar, R., Krogager, J. K., Noterdaeme, P., Solimano, M.
We present VLT/MUSE integral-field spectroscopy ($R\approx 1\,800$) of four giant gravitational arcs exhibiting strong C IV absorption at 8 intervening redshifts, $z_{abs}\approx 2.0$--$2.5$. We detect C IV absorption in a total of 222 adjacent and s
Externí odkaz:
http://arxiv.org/abs/2410.03029
Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers the
Externí odkaz:
http://arxiv.org/abs/2409.20387
In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Re{\tau} = 200. The estimation technique is based on a 3D generative adversarial network (3
Externí odkaz:
http://arxiv.org/abs/2409.07348
Autor:
Cuéllar, Antonio, Güemes, Alejandro, Ianiro, Andrea, Flores, Óscar, Vinuesa, Ricardo, Discetti, Stefano
Publikováno v:
Cu\'ellar, A., G\"uemes, A., Ianiro, A., Flores, \'O., Vinuesa, R., Discetti, S.: Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements. J. Fluid Mech. 991, A1 (2024)
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising me
Externí odkaz:
http://arxiv.org/abs/2409.06548
We propose a set of causal estimands that we call the "mediated probabilities of causation." These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized settin
Externí odkaz:
http://arxiv.org/abs/2404.07397
Reviews conducted by the National Academy of Sciences (2009) and the President's Council of Advisors on Science and Technology (2016) concluded that the field of forensic firearm comparisons has not been demonstrated to be scientifically valid. Scien
Externí odkaz:
http://arxiv.org/abs/2403.17248
Autor:
Alba-Cuellar, Daniel
The LC method described in this work seeks to approximate the roots of polynomial equations in one variable. This book allows you to explore the LC method, which uses geometric structures of Lines L and Circumferences C in the plane of complex number
Externí odkaz:
http://arxiv.org/abs/2402.15554
Autor:
Alba-Cuellar, Daniel
Publikováno v:
Maple Trans. 4, 2, Article 17589 (2024), 13 pages. Publication date: 2024-07-20
This paper describes a geometrical method for finding the roots $r_1$, $r_2$ of a quadratic equation in one complex variable of the form $x^2+c_1 x+c_2=0$, by means of a Line $L$ and a Circumference $C$ in the complex plane, constructed from known co
Externí odkaz:
http://arxiv.org/abs/2402.04385
Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To add
Externí odkaz:
http://arxiv.org/abs/2312.00032