Conventional Valuation and the Term Structure of Interest Rates

There does not appear to be a general tendency for long-term interest rates either to overreact or to underreact to short-term interest rates relative to a rational expectations model of the term structure. Rather, there appears to be some tendency for markets to set long-term interest rates in terms of a convention or rule of thumb that makes long rates behave as a distributed lag, with gradually declining coefficients, of short-term interest rates. People seem to remember the recent past but blur the mare distant. In some monetary policy regimes this convention implies overreaction, in others underreaction.

Thermo-hydraulic Characterization of the Smooth Wavy Fin-and-elliptical Tube Heat Exchangers Using New Type Vortex Generators

AbstractIn the present study, 3D computational analysis was performed to investigate heat transfer and pressure drop characteristics of flow in new Smooth Wavy Fin-and-Elliptical Tube (SWFET) heat exchanger model with new vortex generators. Performance results are presented in terms of non-dimensional parameters, friction factor f and Colburn j factor. Four new types of vortex generators were considered; rectangular trapezoidal winglet (RTW), angle rectangular winglet (ARW), curved angle rectangular winglet (CARW) and Wheeler wishbone (WW). Fluid flow and heat transfer are simulated and the results are compared. The SST k–ω turbulence model is used, with steady-state solvers to calculate pressure drop, flow and temperature fields. The influences of the geometrical factors of mounted vortex generators including attack angles of the winglets (αVG = 15°, 30°, 45°, 60° and 75̊) and width/length aspect ratio (w/l = 0.5,1.0) of the Wheeler wishbones in enhancing the heat transfer performance of a smooth wavy fin heat exchanger with a three-row staggered elliptical tube bundle are investigated. The Reynolds number ranges from 500 to 3000 based on the hydraulic diameter. A parametric study on the winglet vortex generators indicated that for the small attack angle, CARW vortex generators gives better thermohydraulic performance under the present conditions. The best thermal performance of the SWFET heat exchanger with winglet VGs in the larger attack angle, was obtained at RTW VGs arrangement. For the SWFET heat exchangers, the WW VGs with width/length aspect ratio of w/l = 0.5 provide the best heat transfer performance

Coupling of Finite-Element and Plane Waves Discontinuous Galerkin methods for time-harmonic problems

A coupling approach is presented to combine a wave-based method to the standard finite element method. This coupling methodology is presented here for the Helmholtz equation but it can be applied to a wide range of wave propagation problems. While wave-based methods can significantly reduce the computational cost, especially at high frequencies, their efficiency is hindered by the need to use small elements to resolve complex geometric features. This can be alleviated by using a standard Finite-Element Model close to the surfaces to model geometric details and create large, simply-shaped areas to model with a wave-based method. This strategy is formulated and validated in this paper for the wave-based discontinuous Galerkin method together with the standard finite element method. The coupling is formulated without using Lagrange multipliers and results demonstrate that the coupling is optimal in that the convergence rates of the individual methods are maintained

Hybridizable discontinuous Galerkin p-adaptivity for wave propagation problems

A p-adaptive hybridizable discontinuous Galerkin method for the solution of wave problems is presented in a challenging engineering problem. Moreover, its performance is compared with a high-order continuous Galerkin. The hybridization technique allows to reduce the coupled degrees of freedom to only those on the mesh element boundaries, whereas the particular choice of the numerical fluxes opens the path to a superconvergent postprocessed solution. This superconvergent postprocessed solution is used to construct a simple and inexpensive error estimator. The error estimator is employed to obtain solutions with the prescribed accuracy in the area (or areas) of interest and also drives a proposed iterative mesh adaptation procedure. The proposed method is applied to a nonhomogeneous scattering problem in an unbounded domain. This is a challenging problem because, on the one hand, for high frequencies, numerical difficulties are an important issue because of the loss of the ellipticity and the oscillatory behavior of the solution. And on the other hand, it is applied to real harbor agitation problems. That is, the mild slope equation in frequency domain (Helmholtz equation with nonconstant coefficients) is solved on real geometries with the corresponding perfectly matched layer to damp the diffracted waves. The performance of the method is studied on two practical examples. The adaptive hybridizable discontinuous Galerkin method exhibits better efficiency compared with a high-order continuous Galerkin method using static condensation of the interior nodes

Computational Electromagnetism and Acoustics

The challenge inherent in the accurate and efficient numerical modeling of wave propagation phenomena is the common grand theme in both computational electromagnetics and acoustics. Many excellent contributions at this Oberwolfach workshop were devoted to this theme and a wide range of numerical techniques and algorithms were mustered to tackle this challenge

Adaptive hybrid discontinuous methods for fluid and wave problems

This PhD thesis proposes a p-adaptive technique for the Hybridizable Discontinuous Galerkin method (HDG). The HDG method is a novel discontinuous Galerkin method (DG) with interesting characteristics. While retaining all the advantages of the common DG methods, such as the inherent stabilization and the local conservation properties, HDG allows to reduce the coupled degrees of freedom of the problem to those of an approximation of the solution de¿ned only on the faces of the mesh. Moreover, the convergence properties of the HDG solution allow to perform an element-by-element postprocess resulting in a superconvergent solution. Due to the discontinuous character of the approximation in HDG, p-variable computations are easily implemented. In this work the superconvergent postprocess is used to de¿ne a reliable and computationally cheap error estimator, that is used to drive an automatic adaptive process. The polynomial degree in each element is automatically adjusted aiming at obtaining a uniform error distribution below a user de¿ned tolerance. Since no topological modi¿cation of the discretization is involved, fast adaptations of the mesh are obtained. First, the p-adaptive HDG is applied to the solution of wave problems. In particular, the Mild Slope equation is used to model the problem of sea wave propagation is coastal areas and harbors. The HDG method is compared with the continuous Galerkin (CG) ¿nite element method, which is nowadays the common method used in the engineering practice for this kind of applications. Numerical experiments reveal that the e¿ciency of HDG is close to CG for uniform degree computations, clearly outperforming other DG methods such as the Compact Discontinuous Galerkin method. When p-adaptivity is considered, an important saving in computational cost is shown. Then, the methodology is applied to the solution of the incompressible Navier-Stokes equations for the simulation of laminar ¿ows. Both steady state and transient applications are considered. Various numerical experiments are presented, in 2D and 3D, including academic examples and more challenging applications of engineering interest. Despite the simplicity and low cost of the error estimator, high e¿ciency is exhibited for analytical examples. Moreover, even though the adaptive technique is based on an error estimate for just the velocity ¿eld, high accuracy is attained for all variables, with sharp resolution of the key features of the ¿ow and accurate evaluation of the ¿uid-dynamic forces. In particular, high degrees are automatically located along boundary layers, reducing the need for highly distorted elements in the computational mesh. Numerical tests show an important reduction in computational cost, compared to uniform degree computations, for both steady and unsteady computations

Autonomy in Modern Japanese Literature

This dissertation aims to examine the manner in which the concept of autonomy (jiritsu) is treated in modern and contemporary Japanese literature. This examination will be performed by analysing the autonomous attitude of a contemporary Japanese writer Nakagami Kenji (1946–1992). This dissertation focuses on examining Nakagami Kenji’s ambivalent attitude towards his act of writing. We will explore the manner in which his act of writing appears to be a paradox between self-identification and the integration into the collective. Then, we will observe the possibility in which Nakagami’s ambivalent attitude is extended to cover Maruyama Masao’s relative definition of autonomy and Karatani Kōjin’s interpretation of Immanuel Kant’s notion of freedom and responsibility. Nakagami’s attempt is certainly not confined to only his works. The notion of autonomy may be applied to perceive a similar thought that was represented by previous writers. We will also examine various never-ending autonomous attempts expressed by Sakaguchi Ango, Miyazawa Kenji and Nakahara Chūya. Moreover, we will analyse how Nakagami’s distrust of the modern Japanese language and his admiration of the body as an undeniable object are reflected in his major novels in detail and attempt to extend this observation into the works of the theatrical artists in the 1960s such as Betsuyaku Minoru, Kara Jūrō, Hijikata Tatsumi and Terayama Shūji and contemporary women writers such as Tsushima Yūko, Takamura Kaoru, Tawada Yōko and Yoshimoto Banana. These writers and artists struggled to establish their autonomous freedom as they encountered the conflict between their individual bodies that personifies their personal autonomy and the modern Japanese language that confines them in the fixed and submissive roles in present-day Japan. In this dissertation, I would like to conclude that Nakagami Kenji’s ambivalent attitude towards his act of writing can be an eternal self-legislation, that is, his endless attempt to establish autonomous freedom, which evolves from the paradox between the individual (body) and the collective (language)

Étude de la propagation acoustique en milieu complexe par des réseaux de neurones profonds

Abstract : Predicting the propagation of aerocoustic noise is a challenging task in the presence of complex mean flows and geometry installation effects. The design of future quiet propul- sion systems requires tools that are able to perform many accurate evaluations with a low computational cost. Analytical models or hybrid numerical approaches have tradition- ally been employed for that purpose. However, such methods are typically constrained by simplifying hypotheses that are not easily relaxed. Thus, the main objective of this thesis is to develop and validate novel methods for the fast and accurate prediction of aeroacoustic propagation in complex mean flows and geometries. For that, data-driven deep convolutional neural networks acting as auto-regressive spatio-temporal predictors are considered. These surrogates are trained on high-fidelity data, generated by direct aeroacoustic numerical solvers. Such datasets are able to model complex flow phenomena, along with complex geometrical parameters. The neural network is designed to substitute the high-fidelity solver at a much lower computational cost once the training is finished, while predicting the time-domain acoustic propagation with sufficient accuracy. Three test cases of growing complexity are employed to test the approach, where the learned surrogate is compared to analytical and numerical solutions. The first one corresponds to the two-dimensional propagation of Gaussian pulses in closed domains, which allows understanding the fundamental behavior of the employed convolution neural networks. Second, the approach is extended in order to consider a variety of boundary conditions, from non-reflecting to curved reflecting obstacles, including the reflection and scattering of waves at obstacles. This allows the prediction of acoustic propagation in configurations closer to industrial problems. Finally, the effects of complex mean flows is investigated through a dataset of acoustic waves propagating inside sheared flows. These applications highlight the flexibility of the employed data-driven methods using convolutional neural networks. They allow a significant acceleration of the acoustic predictions, while keeping an adequate accuracy and being also able to correctly predict the acoustic propagation outside the range of the training data. For that, prior knowledge about the wave propa- gation physics is included during and after the neural network training phase, allowing an increased control over the error performed by the surrogate. Among this prior knowledge, the conservation of physics quantities and the correct treatment of boundary conditions are identified as key parameters that improve the surrogate predictions.Prédire la propagation du bruit aéroacoustique est une tâche difficile en présence d’écoulements moyens complexes et d’effets géométriques d’installation. La conception des futurs systèmes de propulsion silencieux appelle au développement d’outils capables d’effectuer de nombreuses évaluations avec une faible erreur et un faible coût de calcul. Traditionnellement, des modèles analytiques ou des approches numériques hybrides ont été utilisés à cette fin. Cependant, ces méthodes sont généralement contraintes par des hypothèses simplificatrices qui ne sont pas facilement assouplies. Ainsi, l’objectif principal de cette thèse est de développer et de valider de nouvelles méthodes pour la prédiction rapide et précise de la propagation aéroacoustique dans des écoulements moyens et des géométries complexes. Pour cela, des réseaux de neurones profonds à convolution, entraînés sur des données, et agissant comme prédicteurs spatio-temporels sont considérés. Ces modèles par substitution sont entraînés sur des données de haute fidélité, générées par des solveurs numériques aérocoustiques directs. De telles bases de données sont capables de modéliser des phénomènes d’écoulement, ainsi que des paramètres géométriques complexes. Le réseau de neurones est conçu pour remplacer le solveur haute fidélité à un coût de calcul beaucoup plus faible une fois la phase d’entraînement terminée, tout en prédisant la propagation acoustique dans le domaine temporel avec une précision suffisante. Trois cas de test, de complexité croissante, sont utilisés pour tester l’approche, où le substitut appris est comparé à des solutions analytiques et numériques. Le premier cas correspond à la propagation acoustique bidimensionnelle dans des domaines fermés, où des sources impulsionnelles Gaussiennes sont considérées. Ceci permet de comprendre le comportement fondamental des réseaux de neurones à convolution étudiés. Deuxièmement, l’approche est étendue afin de prendre en compte une variété de conditions aux limites, notamment des conditions aux limites non réfléchissantes et des obstacles réfléchissants de géométrie arbitraire, modélisant la réflexion et la diffusion des ondes acoustiques sur ces obstacles. Cela permet de prédire la propagation acoustique dans des configurations plus proches des problématiques industrielles. Enfin, les effets des écoulements moyens complexes sont étudiés à travers une base de données d’ondes acoustiques qui se propagent à l’intérieur d’écoulements cisaillés. Ces applications mettent en évidence la flexibilité des méthodes basées sur les données, utilisant des réseaux de neurones à convolution. Ils permettent une accélération significative des prédictions acoustiques, tout en gardant une précision adéquate et en étant également capables de prédire correctement la propagation acoustique en dehors de la gamme de paramètres des données d’apprentissage. Pour cela, des connaissances préalables sur la physique de propagation des ondes sont incluses pendant et après la phase d’apprentissage du réseau de neurones, permettant un contrôle accru sur l’erreur effectuée par le substitut. Parmi ces connaissances préalables, la conservation des grandeurs physiques et le traitement correct des conditions aux limites sont identifiés comme des paramètres clés qui améliorent les prédictions du modèle proposé

Hybridizable discontinuous Galerkin p-adaptivity for wave propagation problems

A p-adaptive hybridizable discontinuous Galerkin method for the solution of wave problems is presented in a challenging engineering problem. Moreover, its performance is compared with a high-order continuous Galerkin. The hybridization technique allows to reduce the coupled degrees of freedom to only those on the mesh element boundaries, whereas the particular choice of the numerical fluxes opens the path to a superconvergent postprocessed solution. This superconvergent postprocessed solution is used to construct a simple and inexpensive error estimator. The error estimator is employed to obtain solutions with the prescribed accuracy in the area (or areas) of interest and also drives a proposed iterative mesh adaptation procedure. The proposed method is applied to a nonhomogeneous scattering problem in an unbounded domain. This is a challenging problem because, on the one hand, for high frequencies, numerical difficulties are an important issue because of the loss of the ellipticity and the oscillatory behavior of the solution. And on the other hand, it is applied to real harbor agitation problems. That is, the mild slope equation in frequency domain (Helmholtz equation with nonconstant coefficients) is solved on real geometries with the corresponding perfectly matched layer to damp the diffracted waves. The performance of the method is studied on two practical examples. The adaptive hybridizable discontinuous Galerkin method exhibits better efficiency compared with a high-order continuous Galerkin method using static condensation of the interior nodes.Peer ReviewedPostprint (author’s final draft