Experimental determination of stator endwall heat transfer

Local Stanton numbers were experimentally determined for the endwall surface of a turbine vane passage. A six vane linear cascade having vanes with an axial chord of 13.81 cm was used. Results were obtained for Reynolds numbers based on inlet velocity and axial chord between 73,000 and 495,000. The test section was connected to a low pressure exhaust system. Ambient air was drawn into the test section, inlet velocity was controlled up to a maximum of 59.4 m/sec. The effect of the inlet boundary layer thickness on the endwall heat transfer was determined for a range of test section flow rates. The liquid crystal measurement technique was used to measure heat transfer. Endwall heat transfer was determined by applying electrical power to a foil heater attached to the cascade endwall. The temperature at which the liquid crystal exhibited a specific color was known from a calibration test. Lines showing this specific color were isotherms, and because of uniform heat generation they were also lines of nearly constant heat transfer. Endwall static pressures were measured, along with surveys of total pressure and flow angles at the inlet and exit of the cascade

A moving mesh method for one-dimensional hyperbolic conservation laws

We develop an adaptive method for solving one-dimensional systems of hyperbolic conservation laws that employs a high resolution Godunov-type scheme for the physical equations, in conjunction with a moving mesh PDE governing the motion of the spatial grid points. Many other moving mesh methods developed to solve hyperbolic problems use a fully implicit discretization for the coupled solution-mesh equations, and so suffer from a significant degree of numerical stiffness. We employ a semi-implicit approach that couples the moving mesh equation to an efficient, explicit solver for the physical PDE, with the resulting scheme behaving in practice as a two-step predictor-corrector method. In comparison with computations on a fixed, uniform mesh, our method exhibits more accurate resolution of discontinuities for a similar level of computational work

Big Bang Synthesis of Nuclear Dark Matter

We investigate the physics of dark matter models featuring composite bound states carrying a large conserved dark "nucleon" number. The properties of sufficiently large dark nuclei may obey simple scaling laws, and we find that this scaling can determine the number distribution of nuclei resulting from Big Bang Dark Nucleosynthesis. For plausible models of asymmetric dark matter, dark nuclei of large nucleon number, e.g. > 10^8, may be synthesised, with the number distribution taking one of two characteristic forms. If small-nucleon-number fusions are sufficiently fast, the distribution of dark nuclei takes on a logarithmically-peaked, universal form, independent of many details of the initial conditions and small-number interactions. In the case of a substantial bottleneck to nucleosynthesis for small dark nuclei, we find the surprising result that even larger nuclei, with size >> 10^8, are often finally synthesised, again with a simple number distribution. We briefly discuss the constraints arising from the novel dark sector energetics, and the extended set of (often parametrically light) dark sector states that can occur in complete models of nuclear dark matter. The physics of the coherent enhancement of direct detection signals, the nature of the accompanying dark-sector form factors, and the possible modifications to astrophysical processes are discussed in detail in a companion paper.Comment: 27 pages, 5 figures, v3; minor additional comments - matches published versio

Checkerboard Julia Sets for Rational Maps

In this paper, we consider the family of rational maps \F(z) = z^n + \frac{\la}{z^d}, where $n \geq 2$, $d\geq 1$, and\la \in \bbC. We consider the case where \la lies in the main cardioid of one of the $n-1$ principal Mandelbrot sets in these families. We show that the Julia sets of these maps are always homeomorphic. However, two such maps \F and $F_\mu$ are conjugate on these Julia sets only if the parameters at the centers of the given cardioids satisfy \mu = \nu^{j(d+1)}\la or \mu = \nu^{j(d+1)}\bar{\la} where j \in \bbZ and $\nu$ is an $n-1^{\rm st}$ root of unity. We define a dynamical invariant, which we call the minimal rotation number. It determines which of these maps are are conjugate on their Julia sets, and we obtain an exact count of the number of distinct conjugacy classes of maps drawn from these main cardioids.Comment: 25 pages, 14 figures; Changes since March 19 version: added nine figures, fixed one proof, added a section on a group actio

CATHEDRAL: A Fast and Effective Algorithm to Predict Folds and Domain Boundaries from Multidomain Protein Structures

We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification