Anisotropy in Homogeneous Rotating Turbulence

The effective stress tensor of a homogeneous turbulent rotating fluid is anisotropic. This leads us to consider the most general axisymmetric four-rank ``viscosity tensor'' for a Newtonian fluid and the new terms in the turbulent effective force on large scales that arise from it, in addition to the microscopic viscous force. Some of these terms involve couplings to vorticity and others are angular momentum non conserving (in the rotating frame). Furthermore, we explore the constraints on the response function and the two-point velocity correlation due to axisymmetry. Finally, we compare our viscosity tensor with other four-rank tensors defined in current approaches to non-rotating anisotropic turbulence.Comment: 14 pages, RevTe

Spatially Resolved Temperature and Water Vapor Concentration Distributions in Supersonic Combustion Facilities by TDLAT

Detailed knowledge of the internal structure of high-enthalpy flows can provide valuable insight to the performance of scramjet combustors. Tunable Diode Laser Absorption Spectroscopy (TDLAS) is often employed to measure temperature and species concentration. However, TDLAS is a path-integrated line-of-sight (LOS) measurement, and thus does not produce spatially resolved distributions. Tunable Diode Laser Absorption Tomography (TDLAT) is a non-intrusive measurement technique for determining two-dimensional spatially resolved distributions of temperature and species concentration in high enthalpy flows. TDLAT combines TDLAS with tomographic image reconstruction. More than 2500 separate line-of-sight TDLAS measurements are analyzed in order to produce highly resolved temperature and species concentration distributions. Measurements have been collected at the University of Virginia's Supersonic Combustion Facility (UVaSCF) as well as at the NASA Langley Direct-Connect Supersonic Combustion Test Facility (DCSCTF). Due to the UVaSCF s unique electrical heating and ability for vitiate addition, measurements collected at the UVaSCF are presented as a calibration of the technique. Measurements collected at the DCSCTF required significant modifications to system hardware and software designs due to its larger measurement area and shorter test duration. Tomographic temperature and water vapor concentration distributions are presented from experimentation on the UVaSCF operating at a high temperature non-reacting case for water vitiation level of 12%. Initial LOS measurements from the NASA Langley DCSCTF operating at an equivalence ratio of 0.5 are also presented. Results show the capability of TDLAT to adapt to several experimental setups and test parameters

A CNN and LSTM-based Model for Creating Captions for Photos

Can a machine interpret an image's meaning with the same speed as the human brain when it is seen? This problem was heavily researched by computer vision specialists, who believed it to be unsolvable until recently. It is now possible to develop models that can generate captions for pictures because of advancements in deep learning techniques, accessibility to large datasets, and processing power. This will be accomplished by the Python-based implementation of the article's deep learning convolutional neural network technique and a particular kind of recurrent neural network. Here the proposed model uses CNN and LSTM methods to achieve desired tas

Measurements on NASA Langley Durable Combustor Rig by TDLAT: Preliminary Results

Detailed knowledge of the internal structure of high-enthalpy flows can provide valuable insight to the performance of scramjet combustors. Tunable Diode Laser Absorption Spectroscopy (TDLAS) is often employed to measure temperature and species concentration. However, TDLAS is a path-integrated line-of-sight (LOS) measurement, and thus does not produce spatially resolved distributions. Tunable Diode Laser Absorption Tomography (TDLAT) is a non-intrusive measurement technique for determining two-dimensional spatially resolved distributions of temperature and species concentration in high enthalpy flows. TDLAT combines TDLAS with tomographic image reconstruction. Several separate line-of-sight TDLAS measurements are analyzed in order to produce highly resolved temperature and species concentration distributions. Measurements have been collected at the University of Virginia's Supersonic Combustion Facility (UVaSCF) as well as at the NASA Langley Direct-Connect Supersonic Combustion Test Facility (DCSCTF). Measurements collected at the DCSCTF required significant modifications to system hardware and software designs due to its larger measurement area and shorter test duration. Initial LOS measurements from the NASA Langley DCSCTF operating at an equivalence ratio of 0.5 are presented. Results show the capability of TDLAT to adapt to several experimental setups and test parameters

Digital Camera Control for Faster Inspection

Digital Camera Control Software (DCCS) is a computer program for controlling a boom and a boom-mounted camera used to inspect the external surface of a space shuttle in orbit around the Earth. Running in a laptop computer in the space-shuttle crew cabin, DCCS commands integrated displays and controls. By means of a simple one-button command, a crewmember can view low- resolution images to quickly spot problem areas and can then cause a rapid transition to high- resolution images. The crewmember can command that camera settings apply to a specific small area of interest within the field of view of the camera so as to maximize image quality within that area. DCCS also provides critical high-resolution images to a ground screening team, which analyzes the images to assess damage (if any); in so doing, DCCS enables the team to clear initially suspect areas more quickly than would otherwise be possible and further saves time by minimizing the probability of re-imaging of areas already inspected. On the basis of experience with a previous version (2.0) of the software, the present version (3.0) incorporates a number of advanced imaging features that optimize crewmember capability and efficiency

Anomalous scaling of passively advected magnetic field in the presence of strong anisotropy

Inertial-range scaling behavior of high-order (up to order N=51) structure functions of a passively advected vector field has been analyzed in the framework of the rapid-change model with strong small-scale anisotropy with the aid of the renormalization group and the operator-product expansion. It has been shown that in inertial range the leading terms of the structure functions are coordinate independent, but powerlike corrections appear with the same anomalous scaling exponents as for the passively advected scalar field. These exponents depend on anisotropy parameters in such a way that a specific hierarchy related to the degree of anisotropy is observed. Deviations from power-law behavior like oscillations or logarithmic behavior in the corrections to structure functions have not been found.Comment: 15 pages, 18 figure

Preference-Based Monte Carlo Tree Search

Monte Carlo tree search (MCTS) is a popular choice for solving sequential anytime problems. However, it depends on a numeric feedback signal, which can be difficult to define. Real-time MCTS is a variant which may only rarely encounter states with an explicit, extrinsic reward. To deal with such cases, the experimenter has to supply an additional numeric feedback signal in the form of a heuristic, which intrinsically guides the agent. Recent work has shown evidence that in different areas the underlying structure is ordinal and not numerical. Hence erroneous and biased heuristics are inevitable, especially in such domains. In this paper, we propose a MCTS variant which only depends on qualitative feedback, and therefore opens up new applications for MCTS. We also find indications that translating absolute into ordinal feedback may be beneficial. Using a puzzle domain, we show that our preference-based MCTS variant, wich only receives qualitative feedback, is able to reach a performance level comparable to a regular MCTS baseline, which obtains quantitative feedback.Comment: To be publishe

BoostingTree: parallel selection of weak learners in boosting, with application to ranking

Boosting algorithms have been found successful in many areas of machine learning and, in particular, in ranking. For typical classes of weak learners used in boosting (such as decision stumps or trees), a large feature space can slow down the training, while a long sequence of weak hypotheses combined by boosting can result in a computationally expensive model. In this paper we propose a strategy that builds several sequences of weak hypotheses in parallel, and extends the ones that are likely to yield a good model. The weak hypothesis sequences are arranged in a boosting tree, and new weak hypotheses are added to promising nodes (both leaves and inner nodes) of the tree using some randomized method. Theoretical results show that the proposed algorithm asymptotically achieves the performance of the base boosting algorithm applied. Experiments are provided in ranking web documents and move ordering in chess, and the results indicate that the new strategy yields better performance when the length of the sequence is limited, and converges to similar performance as the original boosting algorithms otherwise. © 2013 The Author(s)