Multi-dimensional modelling of X-ray spectra for AGN accretion-disk outflows III: application to a hydrodynamical simulation

We perform multi-dimensional radiative transfer simulations to compute spectra for a hydrodynamical simulation of a line-driven accretion disk wind from an active galactic nucleus. The synthetic spectra confirm expectations from parameterized models that a disk wind can imprint a wide variety of spectroscopic signatures including narrow absorption lines, broad emission lines and a Compton hump. The formation of these features is complex with contributions originating from many of the different structures present in the hydrodynamical simulation. In particular, spectral features are shaped both by gas in a successfully launched outflow and in complex flows where material is lifted out of the disk plane but ultimately falls back. We also confirm that the strong Fe Kalpha line can develop a weak, red-skewed line wing as a result of Compton scattering in the outflow. In addition, we demonstrate that X-ray radiation scattered and reprocessed in the flow has a pivotal part in both the spectrum formation and determining the ionization conditions in the wind. We find that scattered radiation is rather effective in ionizing gas which is shielded from direct irradiation from the central source. This effect likely makes the successful launching of a massive disk wind somewhat more challenging and should be considered in future wind simulations.Comment: 14 pages, 8 figures. Accepted for publication by MNRA

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Minimal Curvature Trajectories: Riemannian Geometry Concepts for Model Reduction in Chemical Kinetics

In dissipative ordinary differential equation systems different time scales cause anisotropic phase volume contraction along solution trajectories. Model reduction methods exploit this for simplifying chemical kinetics via a time scale separation into fast and slow modes. The aim is to approximate the system dynamics with a dimension-reduced model after eliminating the fast modes by enslaving them to the slow ones via computation of a slow attracting manifold. We present a novel method for computing approximations of such manifolds using trajectory-based optimization. We discuss Riemannian geometry concepts as a basis for suitable optimization criteria characterizing trajectories near slow attracting manifolds and thus provide insight into fundamental geometric properties of multiple time scale chemical kinetics. The optimization criteria correspond to a suitable mathematical formulation of "minimal relaxation" of chemical forces along reaction trajectories under given constraints. We present various geometrically motivated criteria and the results of their application to three test case reaction mechanisms serving as examples. We demonstrate that accurate numerical approximations of slow invariant manifolds can be obtained.Comment: 22 pages, 18 figure

Experiments and simulations of MEMS thermal sensors for wall shear-stress measurements in aerodynamic control applications

MEMS thermal shear-stress sensors exploit heat-transfer effects to measure the shear stress exerted by an air flow on its solid boundary, and have promising applications in aerodynamic control. Classical theory for conventional, macroscale thermal shear-stress sensors states that the rate of heat removed by the flow from the sensor is proportional to the 1/3-power of the shear stress. However, we have observed that this theory is inconsistent with experimental data from MEMS sensors. This paper seeks to develop an understanding of MEMS thermal shear-stress sensors through a study including both experimental and theoretical investigations. We first obtain experimental data that confirm the inadequacy of the classical theory by wind-tunnel testing of prototype MEMS shear-stress sensors with different dimensions and materials. A theoretical analysis is performed to identify that this inadequacy is due to the lack of a thin thermal boundary layer in the fluid flow at the sensor surface, and then a two-dimensional MEMS shear-stress sensor theory is presented. This theory incorporates important heat-transfer effects that are ignored by the classical theory, and consistently explains the experimental data obtained from prototype MEMS sensors. Moreover, the prototype MEMS sensors are studied with three-dimensional simulations, yielding results that quantitatively agree with experimental data. This work demonstrates that classical assumptions made for conventional thermal devices should be carefully examined for miniature MEMS devices

Transition manifolds of complex metastable systems: Theory and data-driven computation of effective dynamics

We consider complex dynamical systems showing metastable behavior but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics

Bringing computational steering to the user

Computational steering is a technique that combines simulation and visualization. The user is continuously provided with visual feedback about the state of the simulation, and can change parameters on the fly. Designers can vary parameters to optimize their product, users can detect errors in the input early, researchers can do qualitative sensitivity analyses easily. The implementation of computational steering is very tedious. It requires knowledge of the simulation, visualization, user interfacing, and data communication. In this paper we discuss an environment that enables users to implement and use computational steering effectively without much support from user interface experts. We show how the environment is applied to various applications