Pipeline column separation flow regimes

A generalized set of pipeline column separation equations is presented describing all conventional types of low-pressure regions. These include water hammer zones, distributed vaporous cavitation, vapor cavities, and shocks (that eliminate distributed vaporous cavitation zones). Numerical methods for solving these equations are then considered, leading to a review of three numerical models of column separation. These include the discrete vapor cavity model, the discrete gas cavity model, and the generalized interface vaporous cavitation model. The generalized interface vaporous cavitation model enables direct tracking of actual column separation phenomena (e.g., discrete cavities, vaporous cavitation zones), and consequently, better insight into the transient event. Numerical results from the three column separation models are compared with results of measurements for a number of flow regimes initiated by a rapid closure of a downstream valve in a sloping pipeline laboratory apparatus. Finally, conclusions are drawn about the accuracy of the modeling approaches. A new classification of column separation (active or passive) is proposed based on whether the maximum pressure in a pipeline following column separation results in a short-duration pressure pulse that exceeds the magnitude of the Joukowsky pressure rise for rapid valve closure.Anton Bergant and Angus R. Simpso

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

The FLASHForward Facility at DESY

The FLASHForward project at DESY is a pioneering plasma-wakefield acceleration experiment that aims to produce, in a few centimetres of ionised hydrogen, beams with energy of order GeV that are of quality sufficient to be used in a free-electron laser. The plasma wave will be driven by high-current density electron beams from the FLASH linear accelerator and will explore both external and internal witness-beam injection techniques. The plasma is created by ionising a gas in a gas cell with a multi-TW laser system, which can also be used to provide optical diagnostics of the plasma and electron beams due to the <30 fs synchronisation between the laser and the driving electron beam. The operation parameters of the experiment are discussed, as well as the scientific program.Comment: 19 pages, 9 figure

Effect of increasing initial implant dosage on feedlot performance and carcass characteristics of long-fed steer and heifer calves1,2

Three experiments evaluated initial implant strategies for finishing cattle. In Exp. 1, heifers (n = 1,405; initial BW = 282 kg) were given (1) Revalor-IH followed by Revalor-200 (REV-IH/200), (2) Revalor-H followed by Revalor-200 (REV-H/200), or (3) Revalor-200 followed by Revalor-200 (REV-200/200). Intake, ADG, and G:F were not affected (P ≥ 0.14) by implant strategies, nor were HCW and LM area (P ≥ 0.16). Percent USDA Choice was greater (P \u3c 0.01) for Rev-IH/200 compared with Rev-H/200 and Rev-200/200. Experiment 2 used steers (n = 1,858; initial BW = 250 kg) given (1) Revalor-IS reimplanted with Revalor-200 (Rev-IS/200), (2) Revalor-XS followed by Revalor-IS (Rev-XS/IS), (3) Revalor-XS followed by Revalor-S (Rev-XS/S), or (4) Revalor-XS followed by Revalor-200 (Rev-XS/200). Implanting strategies did not affect (P ≥ 0.32) DMI or G:F. Carcass traits were not different (P ≥ 0.18) among treatments, except steers implanted with Rev-XS/200 had greater (P \u3c 0.01) LM area. In Exp. 3, steers (n = 1,408; initial BW = 305 kg) were given (1) Rev-IS/200, (2) Rev-200/200, or (3) Rev-XS/200. Gain and G:F did not differ (P ≥ 0.36) among the 3 implant strategies, nor did HCW or marbling score (P ≥ 0.15). Steers given Rev-XS/200 had greater (P \u3c 0.01) LM area and decreased (P ≤ 0.05) 12th-rib fat and YG compared with Rev-200/200 and Rev-IS/200. Using Rev-200/200 and Rev-XS/200 increased (P = 0.03) USDA Select compared with Rev-IS/200. Using greater-initial-dose implant strategies may not affect ADG or G:F but appears to increase leanness

A modification of Honoré's triple-link model in the synoptic problem

In New Testament studies, the synoptic problem is concerned with the relationships between the gospels of Matthew, Mark and Luke. In an earlier paper a careful specification in probabilistic terms was set up of Honoré's triple-link model. In the present paper, a modification of Honoré's model is proposed. As previously, counts of the numbers of verbal agreements between the gospels are examined to investigate which of the possible triple-link models appears to give the best fit to the data, but now using the modified version of the model and additional sets of data