Unsteady adjoint of pressure loss for a fundamental transonic turbine vane

High fidelity simulations, e.g., large eddy simulation are often needed for accurately predicting pressure losses due to wake mixing in turbomachinery applications. An unsteady adjoint of such high fidelity simulations is useful for design optimization in these aerodynamic applications. In this paper we present unsteady adjoint solutions using a large eddy simulation model for a vane from VKI using aerothermal objectives. The unsteady adjoint method is effective in capturing the gradient for a short time interval aerothermal objective, whereas the method provides diverging gradients for long time-averaged thermal objectives. As the boundary layer on the suction side near the trailing edge of the vane is turbulent, it poses a challenge for the adjoint solver. The chaotic dynamics cause the adjoint solution to diverge exponentially from the trailing edge region when solved backwards in time. This results in the corruption of the sensitivities obtained from the adjoint solutions. An energy analysis of the unsteady compressible Navier-Stokes adjoint equations indicates that adding artificial viscosity to the adjoint equations can potentially dissipate the adjoint energy while potentially maintain the accuracy of the adjoint sensitivities. Analyzing the growth term of the adjoint energy provides a metric for identifying the regions in the flow where the adjoint term is diverging. Results for the vane from simulations performed on the Titan supercomputer are demonstrated.Comment: ASME Turbo Expo 201

Multiparticle Bell's inequalities involving many measurement settings

We present a prescription for obtaining Bell's inequalities for N>2 observers involving more than two alternative measurement settings. We give examples of some families of such inequalities. The inequalities are violated by certain classes of states for which all standard Bell's inequalities with two measurement settings per observer are satisfied.Comment: 4 pages, RevTeX

Detection of N-particle entanglement with generalized Bell inequalities

We show that the generalized Bell-type inequality, explicitly involving rotational symmetry of physical laws, is very efficient in distinguishing between true N-particle quantum correlations and correlations involving less particles. This applies to various types of generalized partial separabilities. We also give a rigorous proof that the new Bell inequalities are maximally violated by the GHZ states, and find a very handy description of the N-qubit correlation function.Comment: 5 pages, minor typos corrected, journal versio

Supporting the Algebra I Curriculum with an Introduction to Computational Thinking Course

The Louisiana Workforce Commission predicts a 33.6% increase in computer science and mathematical occupations by 2022 and the Bureau of Labor Statistics foresees a 16% increase in computer scientists from 2018-2028. Despite these opportunities for job and financial security, the number of Louisiana students enrolled in a nationally accredited computing course is less than 1%, compared to national leaders California and Texas which have 3% and 3.8% of students respectively. Furthermore, the international assessments of mathematical literacy, PISA and TIMMS, both report American students continue to fall further behind their international peers in mathematics achievement. This thesis rejects these statistics as definitive and attempts to contribute to an expansion of the mathematical libraries of a computational thinking course that a teacher could use to support a standards-based Algebra I course. The framework presented in this thesis supports the Louisiana State University (LSU) STEM Pathway course entitled Introduction to Computational Thinking (ICT). The course introduces students to a systematic problem-solving approach in which they learn to solve problems computationally, that is, through abstraction, decomposition, and pattern recognition. ICT utilizes the functional programming language Haskell in the educational programming environment “CodeWorld” in order to create pictures and animations. Jean Piaget, the great child cognitive development psychologist, proclaimed “The goal of intellectual education is not to know how to repeat or retain ready-made truths”; rather, one becomes educated by “learning to master the truth by oneself” (Piaget, 1973). Because of the graphical outputs that one can easily code in CodeWorld, students have the ability to explore an algebraic concept with a computer programmed model, alongside the textbook’s given table, equation and graph. This thesis provides additional projects for supporting the Algebra I curriculum through LSU’s ICT course and an overview of the history of computing with an emphasis on highlighting some of the attempts that were undertaken within the past 80 years to use computational thinking and programming to support problem solving across disciplines, including the humanities, math and sciences

A Two-Coordinate Nickel Imido Complex That Effects C−H Amination

An exceptionally low coordinate nickel imido complex, (IPr*)Ni═N(dmp) (2) (dmp = 2,6-dimesitylphenyl), has been prepared by the elimination of N_2 from a bulky aryl azide in its reaction with (IPr*)Ni(η^6-C_7H_8) (1). The solid-state structure of 2 features two-coordinate nickel with a linear C−Ni−N core and a short Ni−N distance, both indicative of multiple-bond character. Computational studies using density functional theory showed a Ni═N bond dominated by Ni(dπ)−N(pπ) interactions, resulting in two nearly degenerate singly occupied molecular orbitals (SOMOs) that are Ni−N π* in character. Reaction of 2 with CO resulted in nitrene-group transfer to form (dmp)NCO and (IPr*)Ni(CO)_3 (3). Net C−H insertion was observed in the reaction of 2 with ethene, forming the vinylamine (dmp)NH(CH═CH_2) (5) via an azanickelacyclobutane intermediate, (IPr*)Ni{N,C:κ^2-N(dmp)CH_2CH_2} (4)

On the volatile acids of cheese

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Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39