Tests of heat shield materials in intense laser radiation

Heat shield materials were tested under intense radiation in a gas dynamic laser. The laser is described and test results are presented

On the Domain of Mixing Angles in Three Flavor Neutrino Oscillations

We clarify the domain needed for the mixing angles in three flavor neutrino oscillations. By comparing the ranges of the transition probabilities as functions of the domains of the mixing angles, we show that it is necessary and sufficient to let all mixing angles be in $[ 0, \pi/2 ]$. This holds irrespectively of any assumptions on the neutrino mass squared differences.Comment: 4 pages, 5 figure

Experimental study of a three dimensional cylinder-filament system

This experimental study reports on the behavior of a filament attached to the rear of a three- dimensional cylinder. The axis of the cylinder is placed normal to a uniform incoming flow and the filament is free to move in the cylinder wake. The mean position of the filament is studied as a function of the filament length L. It is found that for long (L/D > 6.5, where D is the cylinder diameter) and short (L/D < 2) filaments the mean position of the filament tends to align with the incoming flow, whereas for intermediate filament lengths (2 < L/D < 6.5) the filament lies down on the cylinder and tends to align with the cylinder axis. The underlying mechanism of the bifurcations are discussed and related to buckling and inverted-pendulum-like instabilities.Comment: 7 pages, 9 figure

Finite-size spherical particles in a square duct flow of an elastoviscoplastic fluid: an experimental study

The present experimental study addresses the flow of a Yield Stress Fluid with some elasticity (Carbopol gel) in a square duct. The behaviour of two fluids with lower and higher yield stress is investigated at multiple Reynolds numbers $Re^* \in$ (1, 200) and Bingham numbers $Bi \in$ (0.01, 0.35). A secondary flow consisting of eight vortices is observed to recirculate the fluid from the corners to the core. Their extent and intensity grows with increasing $Re^*$. The second objective of this study is to explore the change in flow in the presence of particles. Almost neutrally-buoyant finite-size spherical particles with duct height, $2H$, to particle diameter, $d_p$, ratio of 12 are used at two volume fractions $\phi$ = 5 and 10\%. Particle Tracking Velocimetry (PTV) is used to measure the velocity of these refractive-index-matched spheres, and PIV to extract the fluid velocity. Simple shadowgraphy is also used for qualitatively visualising the development of the particle distribution along the streamwise direction. The particle distribution pattern changes from being concentrated at the four corners, at low flow rates, to being focussed along a diffused ring between the center and the corners, at high flow rates. The presence of particles induces streamwise and wall-normal velocity fluctuations in the fluid phase; however, the primary Reynolds shear stress is still very small compared to turbulent flows. The size of the plug in the particle-laden cases appears to be smaller than the corresponding single phase cases. Similar to Newtonian fluids, the friction factor increases due to the presence of particles, almost independently of the suspending fluid matrix. Interestingly, predictions based on an increased effective suspension viscosity agrees quite well with the experimental friction factor for the concentrations used in this study

Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids)

Redovisning av medel erhållna från Myndigheten för nätverk och samarbete inom högre utbildning. Nätverks benämning: Biomedicin

Nätverksmötena har upplevts som mycket positiva och värdefulla och fler möten planeras. Sammantaget kan sägas att programmen vill verka för att samarbeta för att främja biomedicinarutbildningarnas popularitet och stärka studenternas yrkesidentitet och anställningsbarhet. De olika utbildningsorterna är inte benägna att konkurrera med varandra eller att tävla om att vara den bästa utbildningen eller populäraste utbildningsorten. Man föredrar att komplettera varandra och samverka för ett brett utbud av masterutbildningar

Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.Comment: In 2018 IEEE-RAS International Conference on Humanoid Robots (Humanoids), Beijing, Chin

Effect of weak fluid inertia upon Jeffery orbits

We consider the rotation of small neutrally buoyant axisymmetric particles in a viscous steady shear flow. When inertial effects are negligible the problem exhibits infinitely many periodic solutions, the "Jeffery orbits". We compute how inertial effects lift their degeneracy by perturbatively solving the coupled particle-flow equations. We obtain an equation of motion valid at small shear Reynolds numbers, for spheroidal particles with arbitrary aspect ratios. We analyse how the linear stability of the \lq log-rolling\rq{} orbit depends on particle shape and find it to be unstable for prolate spheroids. This resolves a puzzle in the interpretation of direct numerical simulations of the problem. In general both unsteady and non-linear terms in the Navier-Stokes equations are important.Comment: 5 pages, 2 figure