Thermal Equilibrium Curves and Turbulent Mixing in Keplerian Accretion Disks

We consider vertical heat transport in Keplerian accretion disks, including the effects of radiation, convection, and turbulent mixing driven by the Balbus-Hawley instability, in astronomical systems ranging from dwarf novae (DNe), and soft X-ray transients (SXTs), to active galactic nuclei (AGN). We propose a modified, anisotropic form of mixing-length theory, which includes radiative and turbulent damping. We also include turbulent heat transport, which acts everywhere within disks, regardless of whether or not they are stably stratified, and can move entropy in either direction. We have generated a series of vertical structure models and thermal equilibrium curves using the scaling law for the viscosity parameter $\alpha$ suggested by the exponential decay of the X-ray luminosity in SXTs. We have also included equilibrium curves for DNe using an $\alpha$ which is constant down to a small magnetic Reynolds number ($\sim 10^4$). Our models indicate that weak convection is usually eliminated by turbulent radial mixing. The substitution of turbulent heat transport for convection is more important on the unstable branches of thermal equilibrium S-curves when $\alpha$ is larger. The low temperature turnover points $\Sigma_{max}$ on the equilibrium S-curves are significantly reduced by turbulent mixing in DNe and SXT disks. However, in AGN disks the standard mixing-length theory for convection is still a useful approximation when we use the scaling law for $\alpha$, since these disks are very thin at the relevant radii. In accordance with previous work, we find that constant $\alpha$ models give almost vertical S-curves in the $\Sigma-T$ plane and consequently imply very slow, possibly oscillating, cooling waves.Comment: 43 pages, 12 figures, 6 tables, to be published in Ap

Mixture models for distance sampling detection functions

Funding: EPSRC DTGWe present a new class of models for the detection function in distance sampling surveys of wildlife populations, based on finite mixtures of simple parametric key functions such as the half-normal. The models share many of the features of the widely-used “key function plus series adjustment” (K+A) formulation: they are flexible, produce plausible shapes with a small number of parameters, allow incorporation of covariates in addition to distance and can be fitted using maximum likelihood. One important advantage over the K+A approach is that the mixtures are automatically monotonic non-increasing and non-negative, so constrained optimization is not required to ensure distance sampling assumptions are honoured. We compare the mixture formulation to the K+A approach using simulations to evaluate its applicability in a wide set of challenging situations. We also re-analyze four previously problematic real-world case studies. We find mixtures outperform K+A methods in many cases, particularly spiked line transect data (i.e., where detectability drops rapidly at small distances) and larger sample sizes. We recommend that current standard model selection methods for distance sampling detection functions are extended to include mixture models in the candidate set.Publisher PDFPeer reviewe

Optimal atomic detection by control of detuning and spatial dependence of laser intensity

Atomic detection by fluorescence may fail because of reflection from the laser or transmission without excitation. The detection probability for a given velocity range may be improved by controlling the detuning and the spatial dependence of the laser intensity. A simple optimization method is discussed and exemplified

Parameter estimation in spatially extended systems: The Karhunen-Loeve and Galerkin multiple shooting approach

Parameter estimation for spatiotemporal dynamics for coupled map lattices and continuous time domain systems is shown using a combination of multiple shooting, Karhunen-Loeve decomposition and Galerkin's projection methodologies. The resulting advantages in estimating parameters have been studied and discussed for chaotic and turbulent dynamics using small amounts of data from subsystems, availability of only scalar and noisy time series data, effects of space-time parameter variations, and in the presence of multiple time-scales.Comment: 11 pages, 5 figures, 4 Tables Corresponding Author - V. Ravi Kumar, e-mail address: [email protected]

Actuator Saturation in Individual Blade Control of Rotorcraft

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97053/1/AIAA2012-1477.pd

A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot

Hexapod robots are six-legged robotic systems, which have been widely investigated in the literature for various applications including exploration, rescue, and surveillance. Designing hexapod robots requires to carefully considering a number of different aspects. One of the aspects that require careful design attention is the planning of leg trajectories. In particular, given the high demand for fast motion and high-energy autonomy it is important to identify proper leg operation paths that can minimize energy consumption while maximizing the velocity of the movements. In this frame, this paper presents a preliminary study on the application of a hybrid multi-objective optimization approach for the computer-aided optimal design of a legged robot. To assess the methodology, a kinematic and dynamic model of a leg of a hexapod robot is proposed as referring to the main design parameters of a leg. Optimal criteria have been identified for minimizing the energy consumption and efficiency as well as maximizing the walking speed and the size of obstacles that a leg can overtake. We evaluate the performance of the hybrid multi-objective evolutionary approach to explore the design space and provide a designer with an optimal setting of the parameters. Our simulations demonstrate the effectiveness of the hybrid approach by obtaining improved Pareto sets of trade-off solutions as compared with a standard evolutionary algorithm. Computational costs show an acceptable increase for an off-line path planner. © Springer International Publishing Switzerland 2016

Numerical solution of a pursuit-evasion differential game involving two spacecraft in low earth orbit

This paper considers a spacecraft pursuit-evasion problem taking place in low earth orbit. The problem is formulated as a zero-sum differential game in which there are two players, a pursuing spacecraft that attempts to minimize a payoff, and an evading spacecraft that attempts to maximize the same payoff. We introduce two associated optimal control problems and show that a saddle point for the differential game exists if and only if the two optimal control problems have the same optimal value. Then, on the basis of this result, we propose two computational methods for determining a saddle point solution: a semi-direct control parameterization method (SDCP method), which is based on a piecewise-constant control approximation scheme, and a hybrid method, which combines the new SDCP method with the multiple shooting method. Simulation results show that the proposed SDCP and hybrid methodsare superior to the semi-direct collocation nonlinear programming method (SDCNLP method), which is widely used to solve pursuit-evasion problems in the aerospace field

The cross-entropy method for continuous multi-extremal optimization

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints

Optimal feedback control for dynamic systems with state constraints: An exact penalty approach

In this paper, we consider a class of nonlinear dynamic systems with terminal state and continuous inequality constraints. Our aim is to design an optimal feedback controller that minimizes total system cost and ensures satisfaction of all constraints. We first formulate this problem as a semi-infinite optimization problem. We then show that by using a new exact penalty approach, this semi-infinite optimization problem can be converted into a sequence of nonlinear programming problems, each of which can be solved using standard gradient-based optimization methods.We conclude the paper by discussing applications of our work to glider control

Hybrid optimization method with general switching strategy for parameter estimation

This article is available from: http://www.biomedcentral.com/1752-0509/2/26[Background] Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.[Results] In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.[Conclusion] The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.This work was supported by the European Community as part of the FP6 COSBICS Project (STREP FP6-512060), the German Federal Ministry of Education and Research, BMBF-project FRISYS (grant 0313921) and Xunta de Galicia (PGIDIT05PXIC40201PM).Peer reviewe