New explicit correlation to compute the friction factor under turbulent flow in pipes

HIGHLIGHTS The correlation facilitates the calculation of head losses in hydraulic systems. The correlation was design for regimes of high and low turbulence. The best performance of the correlation is obtained for a range of roughness that goes from 10-2 to 5 × 10-3.

A Novel Simplification for the Prediction of Natural Gas Compressibility Factor

The need for a simpler, effective and less expensive predictive tool for the estimation of natural gas compressibility factor cannot be exaggerated. An accurate prediction of gas compressibility factor is essential because it plays a definitive role in evaluating gas reservoir properties used in the estimation of gas reserves, custody transfer and design of surface equipment. In this present work, a novel explicit correlation and a highly sophisticated computer program were developed to accurately predict natural gas deviation factor. The research also aims to effectively capture the relationship between Pseudo-reduced temperature and pressure in relations to the Z-factor. In this study, 3972 digitized data points extracted from Standing and Katz’s Chart were regressed and analyzed using Microsoft Excel Spreadsheet, the extraction of this data was done using WebPlotDigitizer developed by Ankit Rohatgi of GitHub, Pacifica, CA, USA. The correlation was developed as a function of Pseudo-reduced temperature and pressure with tuned parameters distributed across 1.05 ≤ Tpr ≤ 3.0 and 0 < Ppr ≤ 8.0. Subsequently, the input (Tpr and Ppr values) of the feed data was used to validate the correlation and compare it with other known and published correlations. Statistical analysis of the results showed that a 99.8% agreement exists between the predicted and actual compressibility factors for the various test scenarios and case studies involving both sweet and sour gases. Also, the correlation was observed to outperform other models. Finally, the results were observed to perfectly mimic the Standing and Katz charts with an overall correlation coefficient of 99.76% and an adjusted R2 of 99.75%. The proposed correlation was subsequently used to develop a software using JavaScript. Undoubtedly, the proposed correlation and software are suitable for rapid and accurate simplification and prediction of natural gas compressibility factor.

Soil fungal and bacterial communities in southern boreal forests of the Greater Khingan Mountains and their relationship with soil properties

Little is known about the relationship between soil microbial communities and soil properties in southern boreal forests. To further our knowledge about that relationship, we compared the soil samples in southern boreal forests of the Greater Khingan Mountains—the southernmost boreal forest biome in the world. The forests can be divided into boardleaf forests dominated by birch (Betula platyphylla) or aspen (Populus davidiana) and coniferous forests dominated by larch (Larix gmelinii) or pine (Pinus sylvestris var. mongolica). Results suggested different soil microbial communities and soil properties between these southern boreal forests. Soil protease activity strongly associated with soil fungal communities in broadleaf and coniferous forests (p < 0.05), but not with soil bacterial communities (p > 0.05). Soil ammonium nitrogen and total phosphorus contents strongly associated with soil fungal and bacterial communities in broadleaf forests (p < 0.05), but not in coniferous forests (p > 0.05). Soil potassium content demonstrated strong correlations with both soil fungal and bacterial communities in broadleaf and coniferous forests (p < 0.05). These results provide evidence for different soil communities and soil properties in southern boreal forest, and further elucidate the explicit correlation between soil microbial communities and soil properties in southern boreal forests.

Learning Class-Specific Features with Class Regularization for Videos

One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimized as general feature extractors. Distinction between classes is typically left until the very last fully-connected layers. Consequently, variances between classes that are relatively similar are treated the same way as variations between classes that exhibit great dissimilarities. In order to directly address this problem, we introduce Class Regularization, a novel method that can regularize feature map activations based on the classes of the examples used. Essentially, we amplify or suppress activations based on an educated guess of the given class. We can apply this step to each minibatch of activation maps, at different depths in the network. We demonstrate that this improves feature search during training, leading to systematic improvement gains on the Kinetics, UCF-101, and HMDB-51 datasets. Moreover, Class Regularization establishes an explicit correlation between features and class, which makes it a perfect tool to visualize class-specific features at various network depths.

Approximating unstable operation speeds of automatic ball balancers based on design parameters

Automatic balancers present a modular possibility to counteract variable rotor unbalances during operation. Two or more balancing masses, usually spheres, can orbit in a fluid-filled annular cavity whose axis of symmetry coincides with the rotor axis. At supercritical speeds the masses -- driven by the rotor deflection -- tend towards stationary positions inside the cavity opposing the primary rotor unbalance.Related to the phenomenon of rotating shafts being captured at resonances due to insufficient drive power, automatic ball balancers inhibit operation speed bands with non-synchronous vibrations where the rotor surpassed the resonance but the balls continue to orbit with the eigenfrequency with respect to the inertial system. As a result, the balancing masses do not take stationary positions inside the cavity and the rotor is excited not only by the primary unbalance but also by the sub-synchronously orbiting balancing masses.The width of the operation speed band exhibiting non-synchronous behaviour depends on the balancing masses, the orbit radius, external damping of the rotor and viscous damping of the balls due to the fluid inside the cavity. For a planar oscillator in isotropic supports with a balancer containing two balancing balls, an explicit correlation between the stability border and the fluid damping is presented. In order to parameterize the fluid damping model, the drag on spheres in annular cavities is examined and a proposed relation based on the cavity geometry and the fluid properties is presented.

Explicit Correlation Amplifiers for Finding Outlier Correlations in Deterministic Subquadratic Time

We derandomize Valiant’s (J ACM 62, Article 13, 2015) subquadratic-time algorithm for finding outlier correlations in binary data. This demonstrates that it is possible to perform a deterministic subquadratic-time similarity join of high dimensionality. Our derandomized algorithm gives deterministic subquadratic scaling essentially for the same parameter range as Valiant’s randomized algorithm, but the precise constants we save over quadratic scaling are more modest. Our main technical tool for derandomization is an explicit family of correlation amplifiers built via a family of zigzag-product expanders by Reingold et al. (Ann Math 155(1):157–187, 2002). We say that a function $$f:\{-1,1\}^d\rightarrow \{-1,1\}^D$$ f : { - 1 , 1 } d → { - 1 , 1 } D is a correlation amplifier with threshold $$0\le \tau \le 1$$ 0 ≤ τ ≤ 1 , error $$\gamma \ge 1$$ γ ≥ 1 , and strength p an even positive integer if for all pairs of vectors $$x,y\in \{-1,1\}^d$$ x , y ∈ { - 1 , 1 } d it holds that (i) $$|\langle x,y\rangle |<\tau d$$ | ⟨ x , y ⟩ | < τ d implies $$|\langle f(x),f(y)\rangle |\le (\tau \gamma )^pD$$ | ⟨ f ( x ) , f ( y ) ⟩ | ≤ ( τ γ ) p D ; and (ii) $$|\langle x,y\rangle |\ge \tau d$$ | ⟨ x , y ⟩ | ≥ τ d implies $$\left (\frac{\langle x,y\rangle }{\gamma d}\right )^pD \le \langle f(x),f(y)\rangle \le \left (\frac{\gamma \langle x,y\rangle }{d}\right )^pD$$ ⟨ x , y ⟩ γ d p D ≤ ⟨ f ( x ) , f ( y ) ⟩ ≤ γ ⟨ x , y ⟩ d p D .

Micro-Texture Analyses of a Cold-Work Tool Steel for Additive Manufacturing

Small objects of an alloy tool steel were built by selective laser melting at different scan speeds, and their microstructures were analyzed using electron backscatter diffraction (EBSD). To present an explicit correlation with the local thermal cycles in the objects, prior austenite grains were reconstructed using the EBSD mapping data. Extensive growth of austenitic grains after solidification could be detected by the disagreement between the networks of carbides and austenite grain boundaries. A rapid laser scan at 2000 mm/s led to less growth, but retained a larger amount of austenite than a slow one at 50 mm/s. The rapid scan also exhibited definite evolution of Goss-type textures in austenite, which could be attributed to the growth of austenitic grains under a steep temperature gradient. The local variations in the microstructures and the textures enabled us to speculate the locally different thermal cycles determined by the different process conditions, that is, scan speeds.

SiS formation via gas phase reactions between atomic silicon and sulphur-bearing species

ABSTRACT The potential energy surface for the Si + SH and Si + SH2 reactions is explored using the highly accurate explicit correlation multireference configuration interaction method. For atomic silicon colliding with SH, SiS + H is predicted to be the main reaction channel with no activation energy. The reaction Si + SH2 → SiS + H2 is found to be largely thermodynamically favourable, but likely to be slow, due to its spin forbidden nature. Several details on possible mechanisms are evaluated, and implications for astrochemical models are discussed. Among other results, we show that SiS is stable towards collisions with H and H2, and that the HSiS molecule will quickly be converted to SiS in collisons with atomic hydrogen.