Central Compact-Reconstruction WENO Methods

High-order compact upwind schemes produce block-tridiagonal systems due to performing the reconstruction in the characteristic variables, which is necessary to avoid spurious oscillations. Consequently they are less efficient than their non-compact counterparts except on high-frequency features. Upwind schemes lead to many practical drawbacks as well, so it is desirable to have a compact scheme that is more computationally efficient at all wavenumbers that does not require a characteristic decomposition. This goal cannot be achieved by upwind schemes so we turn to the central schemes, which by design require neither a Riemann solver nor a determination of upwind directions by characteristic decomposition. In practice, however, central schemes of fifth or higher order apparently need the characteristic decomposition to fully avoid spurious oscillations. The literature provides no explanation for this fact that is entirely convincing; however, a comparison of two central WENO schemes suggests one. Pursuing that possibility leads to the first main contribution of this work, which is the development of a fifth-order, central compact-reconstruction WENO (CCRWENO) method. That method retains the key advantages of central and compact schemes but does not entirely avoid characteristic variables as was desired. The second major contribution is to establish that the role of characteristic variables is to to make flux Jacobians within a stencil more diagonally dominant, having ruled out some plausible alternative explanations. The CCRWENO method cannot inherently improve the diagonal dominance without compromising its key advantages, so some strategies are explored for modifying the CCRWENO solution to prevent the spurious oscillations. Directions for future investigation and improvement are proposed

The Influence of the Numerical Scheme in Predictions of Vortex Interaction about a Generic Missile Airframe

Vortex dominated flows provide a challenge for modern computational fluid dynamics (CFD) solvers to accurately predict and capture the true physics of the flow. Modeling the formation and propagation of vortices downstream, as well as interactions with other vortices and shocks, are affected by a number of decisions including but not limited to the mesh generated, numerical scheme employed, and modelling assumptions. A research program has been underway for four years to study vortex interaction aerodynamics that are relevant to military air vehicle performance. The program has been conducted under the auspices of the NATO Science and Technology Organization (STO), Applied Vehicle Technology (AVT) panel by a Task Group with the identification of AVT-316. The paper at hand will look at the OTC1 test case [1], which is comprised of a generic missile configuration in supersonic flow, and the influence of the numerical scheme on the predicted results. The decision of the spatial discretization scheme, order of the turbulent flux, and choice of limiter were all shown to have strong influence on the predicted rolling moment of the OTC1 test case

Oil Crisis, Energy-Saving Technological Change and the Stock Market Crash of 1973-74

The market value of U.S. corporations was nearly halved during the oil crisis of 1973-74. In this paper, we investigate the hypothesis that the sharp rise in energy costs during this period resulted in the obsolescence of firms' existing capital and reduced their market value. To quantify this obsolescence channel of the energy crisis, we simulate a calibrated dynamic general equilibrium model, where firms adopt energy-saving technologies along with the rise in energy prices, and the value of their installed capital falls due to investment irreversibility. We find that this channel can account for a third of the decline in Tobin's q observed in the data. Separately, we consider the role of investment subsidies extended by the government during this period to expedite the adoption of energy-saving technologies. This extension of the model can account for more than half of the decline in q. We also find empirical support for the capital obsolescence channel in cross-sectional regressions, where we show that the sectoral variation in the decline of energy use following the crisis is significant in explaining the sectoral variation in the drop of market values. (Copyright: Elsevier)Oil crisis; Capital obsolescence; Stock market crash

Bayesian Graphical Models for STructural Vector Autoregressive Processes

This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced-form VAR model. The BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension, as those considered in the economic literature. In the macroeconomic application the BGVAR identifies the relevant structural relationships among 20 US economic variables, thus providing a useful tool for policy analysis. The financial application contributes to the recent econometric literature on financial interconnectedness. The BGVAR approach provides evidence of a strong unidirectional linkage from financial to non-financial super-sectors during the 2007-2009 financial crisis and a strong bidirectional linkage between the two sectors during the 2010-2013 European sovereign debt crisis. Copyright (c) 2015John Wiley & Sons, Ltd