University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Heater Orientation Effects on Pool Boiling of Micro-Porous-Enhanced Surfaces in Saturated FC-72

Abstract: Experiments are performed to understand the effects of surface orientation on the pool boiling characteristics of a highly wetting fluid from a flush-mounted, micro-porous-enhanced square heater Micro-porous enhancement was achieved by applying copper and aluminum particle coatings to the heater surfaces Effects of heater orientation on CHF and nucleate boiling heat transfer for uncoated and coated surfaces are compared A correlation is developed to predict the heater orientation effect on CHF for those surfaces

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Abstract: . Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

Measurement of the inclusive isolated prompt photon cross section in pp collisions at √s=7TeV with the ATLAS detector

Abstract: A measurement of the cross section for the inclusive production of isolated prompt photons in pp collisions at a center-of-mass energy root s = 7 TeV is presented. The measurement covers the pseudorapidity ranges vertical bar eta(gamma)vertical bar < 1: 37 and 1: 52 <= vertical bar eta(gamma)vertical bar < 1: 81 in the transverse energy range 15 <= E-T(gamma) < 100 GeV. The results are based on an integrated luminosity of 880 nb(-1), collected with the ATLAS detector at the Large Hadron Collider. Photon candidates are identified by combining information from the calorimeters and from the inner tracker. Residual background in the selected sample is estimated from data based on the observed distribution of the transverse isolation energy in a narrow cone around the photon candidate. The results are compared to predictions from next-to-leading-order perturbative QCD calculations.

Open-loop versus closed-loop control of MEMS devices: choices and issues

Abstract: From a controls point of view, micro electromechanical systems (MEMS) can be driven in an open-loop and closed-loop fashion. Commonly, these devices are driven open-loop by applying simple input signals. If these input signals become more complex by being derived from the system dynamics, we call such control techniques pre-shaped open-loop driving. The ultimate step for improving precision and speed of response is the introduction of feedback, e.g. closed-loop control. Unlike macro mechanical systems, where the implementation of the feedback is relatively simple, in the MEMS case the feedback design is quite problematic, due to the limited availability of sensor data, the presence of sensor dynamics and noise, and the typically fast actuator dynamics. Furthermore, a performance comparison between open-loop and closed-loop control strategies has not been properly explored for MEMS devices. The purpose of this paper is to present experimental results obtained using both open- and closed-loop strategies and to address the comparative issues of driving and control for MEMS devices. An optical MEMS switching device is used for this study. Based on these experimental results, as well as computer simulations, we point out advantages and disadvantages of the different control strategies, address the problems that distinguish MEMS driving systems from their macro counterparts, and discuss criteria to choose a suitable control driving strategy.