Subjective Well-Being, Income and Economic Margins

This paper uses the Swedish Level of Living Survey to study how satisfaction with living conditions and daily life covary with economic resources, in the cross-section and in a decade-long panel. We find that self-reported lack of economic margins is a powerful determinant of satisfaction, its magnitude being comparable even to that of marriage or cohabitation. In contrast, although income is positively associated with satisfaction, the relationship is less robust than for economic margins, and the estimated gradients vary substantially depending on the choice of satisfaction measure, income measure and model specification.-

Reference data for phase diagrams of triangular and hexagonal bosonic lattices

We investigate systems of bosonic particles at zero temperature in triangular and hexagonal optical lattice potentials in the framework of the Bose-Hubbard model. Employing the process-chain approach, we obtain accurate values for the boundaries between the Mott insulating phase and the superfluid phase. These results can serve as reference data for both other approximation schemes and upcoming experiments. Since arbitrary integer filling factors g are amenable to our technique, we are able to monitor the behavior of the critical hopping parameters with increasing filling. We also demonstrate that the g-dependence of these exact parameters is described almost perfectly by a scaling relation inferred from the mean-field approximation.Comment: 6 pages, 5 figures, accepted for publication in EP

A high-order semi-explicit discontinuous Galerkin solver for 3D incompressible flow with application to DNS and LES of turbulent channel flow

We present an efficient discontinuous Galerkin scheme for simulation of the incompressible Navier-Stokes equations including laminar and turbulent flow. We consider a semi-explicit high-order velocity-correction method for time integration as well as nodal equal-order discretizations for velocity and pressure. The non-linear convective term is treated explicitly while a linear system is solved for the pressure Poisson equation and the viscous term. The key feature of our solver is a consistent penalty term reducing the local divergence error in order to overcome recently reported instabilities in spatially under-resolved high-Reynolds-number flows as well as small time steps. This penalty method is similar to the grad-div stabilization widely used in continuous finite elements. We further review and compare our method to several other techniques recently proposed in literature to stabilize the method for such flow configurations. The solver is specifically designed for large-scale computations through matrix-free linear solvers including efficient preconditioning strategies and tensor-product elements, which have allowed us to scale this code up to 34.4 billion degrees of freedom and 147,456 CPU cores. We validate our code and demonstrate optimal convergence rates with laminar flows present in a vortex problem and flow past a cylinder and show applicability of our solver to direct numerical simulation as well as implicit large-eddy simulation of turbulent channel flow at $Re_{\tau}=180$ as well as $590$.Comment: 28 pages, in preparation for submission to Journal of Computational Physic

Experimental Slip-based Road Condition Estimation

Heavy traffic loads on the California highways have given birth to the development of automated highways. With vehicles traveling without human interaction, tighter spacing between cars canbeachieved without jeopardizng safety, leading to improved highway throughput. Since no human driver is present to make judgements about velocity and spacing, knowing the road condition is important in order to maintain safety. This project aims to, based on experimental measurements, give information about the road condition, and in this thesis a slip-based method is used. Slip is defined as the relative difference in velocity between the wheels and the vehicle. The data acquired from a Lincoln Towncar introduced di°culties due to very noisy measurements. A number of different approaches of extracting road surface information from the noisy slip data was examined and an observer was developed that signifcantly reduced unwanted effects caused by tire elasticity. The resulting road classifier could distinguish between dry and wet asphalt roads with 16% error probability. The classifier did only work for newly wet roads, most likely since roads are known to be the most slippery right after it has started to rain

Holistic System-Analytics as an Alternative to Isolated Sensor Technology: A Condition Monitoring Use Case

Sensor technology has become increasingly important (e.g., Industry 4.0, IoT). Large numbers of machines and products are equipped with sensors to constantly monitor their condition. Usually, the condition of an entire system is inferred through sensors in parts of the system by means of a multiplicity of methods and techniques. This so-called condition monitoring can thus reduce the downtime costs of a machine through improved maintenance scheduling. However, for small components as well as relatively inexpensive or immutable parts of a machine, sometimes it is not possible or uneconomical to embed sensors. We propose a system-oriented concept of how to monitor individual components of a complex technical system without including additional sensor technology. By using already existing sensors from the environment combined with machine learning techniques, we are able to infer the condition of a system component, without actually observing it. In consequence condition monitoring or additional services based on the component\u27s behavior can be developed without overcoming the challenges of sensor implementation