Finite element analysis of radial stress distribution on axisymmetric variable thickness Dual Mass Flywheel using ANSYS.

Flywheels are applied in storing inertial energy in rotating machine engines and to limit speed fluctuations. In Dual Mass flywheel (DMF) the rotating mass is split into two and is joined by a damping mechanism. It is commonly in hardest use during engine start up and shut down. In flywheel design, important aspects to consider include geometry (cross-section), rotational speed and material strength. Also, to consider is the mass moment of inertia which when too much the system will be sluggish and unresponsive whereas when too little the system would lose momentum over time. The material strength directly determines the energy level that can be produced safely when coupled with rotor speed. This together with rotational speed result to the flywheel being very highly stressed hence necessary to determine stresses accurately using a discrete method as provided for by ANSYS software. During shaft rotation, centrifugal forces generate stresses in the circumferential as well as radial directions. This paper describes studies on the analysis of axisymmetric solid DMF geometry under radial stress distribution at high revolution using ANSYS. Finally, a discussion of the generated results which would be applied in modifications of existing structures for improved new operating service conditions and academic instruction. Keywords: DMF, radial stress, FEA analysis, axisymmetric load

Static structure analysis of 5000tpd Rotary cement kiln using ANSYS Mechanical APDL

Rotary cement kiln is regarded as the heart of cement manufacture in any cement plant widely used to convert raw material into clinker. The capacity of a plant is determined by the production in the kiln whose sizes can be very large to handle higher production capacities of as much as 10000 tonnes per day (tpd) of raw meal to be processed. The ultimate cement quality is determined at the kiln. The varied physical process operations occurring and the equipment's complex construction with required strength necessitates in-depth analysis of static structural aspects for optimized efficiency in performance. In this paper, a 5m diameter 72m length kiln is designed in Pro-E to determined structural total weight being an assembly of components. Its then modeled and analyzed in ANSYS using Mechanical apdl being a statically indeterminate system where kiln stresses distribution status on the cylinder shell is difficult to obtain through general analytical solution methods.  Following the analyzed results, a relevant conclusion is given where the analysis results would be useful in design of rotary kiln cylinder optimization for excellent performance and scholarly work. Keywords; Rotary kiln, Finite element analysis, von-mises stress, tyre

Taming the Runaway Problem of Inflationary Landscapes

A wide variety of vacua, and their cosmological realization, may provide an explanation for the apparently anthropic choices of some parameters of particle physics and cosmology. If the probability on various parameters is weighted by volume, a flat potential for slow-roll inflation is also naturally understood, since the flatter the potential the larger the volume of the sub-universe. However, such inflationary landscapes have a serious problem, predicting an environment that makes it exponentially hard for observers to exist and giving an exponentially small probability for a moderate universe like ours. A general solution to this problem is proposed, and is illustrated in the context of inflaton decay and leptogenesis, leading to an upper bound on the reheating temperature in our sub-universe. In a particular scenario of chaotic inflation and non-thermal leptogenesis, predictions can be made for the size of CP violating phases, the rate of neutrinoless double beta decay and, in the case of theories with gauge-mediated weak scale supersymmetry, for the fundamental scale of supersymmetry breaking.Comment: 31 pages, including 3 figure

Preconditioned Spectral Descent for Deep Learning

Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not address the non-convexity, we present an optimization solution that exploits the so far unused “geometry” in the objective function in order to best make use of the estimated gradients. Previous work attempted similar goals with precon- ditioned methods in the Euclidean space, such as L-BFGS, RMSprop, and ADA- grad. In stark contrast, our approach combines a non-Euclidean gradient method with preconditioning. We provide evidence that this combination more accurately captures the geometry of the objective function compared to prior work. We theo- retically formalize our arguments and derive novel preconditioned non-Euclidean algorithms. The results are promising in both computational time and quality when applied to Restricted Boltzmann Machines, Feedforward Neural Nets, and Convolutional Neural Nets

Exactly Solvable Model of Superstring in Plane-wave Background with Linear Null Dilaton

In this paper, we study an exactly solvable model of IIB superstring in a time-dependent plane-wave backgound with a constant self-dual Ramond-Ramond 5-form field strength and a linear dilaton in the light-like direction. This background keeps sixteen supersymmetries. In the light-cone gauge, the action is described by the two-dimensional free bosons and fermions with time-dependent masses. The model could be canonically quantized and its Hamiltonian is time-dependent with vanishing zero point energy. The spectrum of the excitations is symmetric between the bosonic and fermionic sector. The string mode creation turns out to be very small.Comment: 35 pages, Latex; Acknowledgement added; Published versio

Stochastic Spectral Descent for Discrete Graphical Models

Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art perfor- mance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a significant number of iterations to converge. Since the computational cost of gradient estimation is prohibitive even for modestly-sized models, training becomes slow and practically- usable models are kept small. In this paper we propose a new, largely tuning-free algorithm to address this problem. Our approach derives novel majorization bounds based on the Schatten-∞ norm. Intriguingly, the minimizers of these bounds can be interpreted as gradient methods in a non-Euclidean space. We thus propose using a stochastic gradient method in non-Euclidean space. We both provide simple conditions under which our algorithm is guaranteed to converge, and demonstrate empirically that our algorithm leads to dramatically faster training and improved predictive ability compared to stochastic gradient descent for both directed and undirected graphical models

Freeze-In Production of FIMP Dark Matter

We propose an alternate, calculable mechanism of dark matter genesis, "thermal freeze-in," involving a Feebly Interacting Massive Particle (FIMP) interacting so feebly with the thermal bath that it never attains thermal equilibrium. As with the conventional "thermal freeze-out" production mechanism, the relic abundance reflects a combination of initial thermal distributions together with particle masses and couplings that can be measured in the laboratory or astrophysically. The freeze-in yield is IR dominated by low temperatures near the FIMP mass and is independent of unknown UV physics, such as the reheat temperature after inflation. Moduli and modulinos of string theory compactifications that receive mass from weak-scale supersymmetry breaking provide implementations of the freeze-in mechanism, as do models that employ Dirac neutrino masses or GUT-scale-suppressed interactions. Experimental signals of freeze-in and FIMPs can be spectacular, including the production of new metastable coloured or charged particles at the LHC as well as the alteration of big bang nucleosynthesis.Comment: 30 pages, 7 figures, PDFLaTex. References adde

Knot Theory: from Fox 3-colorings of links to Yang-Baxter homology and Khovanov homology

This paper is an extended account of my "Introductory Plenary talk at Knots in Hellas 2016" conference We start from the short introduction to Knot Theory from the historical perspective, starting from Heraclas text (the first century AD), mentioning R.Llull (1232-1315), A.Kircher (1602-1680), Leibniz idea of Geometria Situs (1679), and J.B.Listing (student of Gauss) work of 1847. We spend some space on Ralph H. Fox (1913-1973) elementary introduction to diagram colorings (1956). In the second section we describe how Fox work was generalized to distributive colorings (racks and quandles) and eventually in the work of Jones and Turaev to link invariants via Yang-Baxter operators, here the importance of statistical mechanics to topology will be mentioned. Finally we describe recent developments which started with Mikhail Khovanov work on categorification of the Jones polynomial. By analogy to Khovanov homology we build homology of distributive structures (including homology of Fox colorings) and generalize it to homology of Yang-Baxter operators. We speculate, with supporting evidence, on co-cycle invariants of knots coming from Yang-Baxter homology. Here the work of Fenn-Rourke-Sanderson (geometric realization of pre-cubic sets of link diagrams) and Carter-Kamada-Saito (co-cycle invariants of links) will be discussed and expanded. Dedicated to Lou Kauffman for his 70th birthday.Comment: 35 pages, 31 figures, for Knots in Hellas II Proceedings, Springer, part of the series Proceedings in Mathematics & Statistics (PROMS

Gene Expression Profiling of Biological Pathway Alterations by Radiation Exposure

[[abstract]]Though damage caused by radiation has been the focus of rigorous research, the mechanisms through which radiation exerts harmful effects on cells are complex and not well-understood. In particular, the influence of low dose radiation exposure on the regulation of genes and pathways remains unclear. In an attempt to investigate the molecular alterations induced by varying doses of radiation, a genome-wide expression analysis was conducted. Peripheral blood mononuclear cells were collected from five participants and each sample was subjected to 0.5 Gy, 1 Gy, 2.5 Gy, and 5 Gy of cobalt 60 radiation, followed by array-based expression profiling. Gene set enrichment analysis indicated that the immune system and cancer development pathways appeared to be the major affected targets by radiation exposure. Therefore, 1 Gy radioactive exposure seemed to be a critical threshold dosage. In fact, after 1 Gy radiation exposure, expression levels of several genes including FADD, TNFRSF10B, TNFRSF8, TNFRSF10A, TNFSF10, TNFSF8, CASP1, and CASP4 that are associated with carcinogenesis and metabolic disorders showed significant alterations. Our results suggest that exposure to low-dose radiation may elicit changes in metabolic and immune pathways, potentially increasing the risk of immune dysfunctions and metabolic disorders.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子

Grand Unification Signal from Ultra High Energy Cosmic Rays?

The spectrum of ultrahigh energy (above \approx 10^{9} GeV) cosmic rays is consistent with the decay of GUT scale particles. The predicted mass is m_X=10^b GeV, where b=14.6_{-1.7}^{+1.6}.Comment: 4 pages, 3 figures one figure removed, one table added, conclusions essentially remained the same within errorbar