Design of Foundations for Wind Turbines

The Swedish government has specified a goal for the Swedish wind power that in 2020 it will generate 30 TWh of energy per year. This should be compared with the present energy produced from wind power of 2.5 TWh / year. To meet these goals, several thousand new wind turbines have to be built. Today, we build the most land-based wind turbines on strong and stiff soils, but probably in the future wind turbines will have to be built also on soils with less good properties. The ordinary and fairly simple foundation method with a concrete slab with large area, may be abandoned since it can give too large differential settlement. This thesis is examining the foundations for onshore wind turbines where both the more convential method with a large concrete slab are investigated, but also alternative foundation methods are studied, mainly piled foundations. Different types of foundations is presented and discussed in which the design procedure consists of both manual calculations and numerical analyses. A case study of an 80 meter high wind turbine with realistic loads is presented. The study includes geotechnical and structural design for three different soil profiles, in which three different foundation methods are used. The three cases are: 1. Strong and stiff moraine soil in which the most common foundation method with a spread foundation is used. 2. A 20 m thick layer of clay that overlay the strong bedrock in which toe-bearing precast concrete piles are used. In this case only the piles are assumed to bear the load. 3. Clay soil with the bedrock at considerable depth in which precast concrete piles are used as cohesion piles. Both piles and the concrete slab are assumed to bear load in a so-called piled-raft foundation. For the three cases above, the same foundation slab is used, but for case 2 and 3 the slab is cast on piles. The results of this study show that all three above-mentioned foundation methods are feasible, but for the third case the differential settlements are significantly big resulting in a horizontal displacement of the tower's top of 155 mm. The first case is the cheapest and easiest to perform, and is preferred if the geotechnical conditions permit that. The second case results in a relative small total pile length of 680 m, while the third case results in 3720 m in total pile length. The big pile length that the third case results in is an expensive and laborious foundation to construct and such should not be constructed. The design of a foundation of this type has many difficulties. In this thesis the geotechnical design was performed using a two-dimensional model in a finite element program for geotechnical applications. Modeling of piles in two dimensions is difficult to do in a realistic way and a three-dimensional model is preferred. This, together with the difficulty of finding the right stiffness ratio between the piles and the plate can be two sources of possible error in the extremely large pile length found for case 3

Scale sensitivity and question order in the contingent valuation method

Agency is gratefully acknowledged. The usual disclaimers apply. Abstract: This study examines the effect on respondents ' willingness to pay to reduce mortality risk by the order of the question in a stated preference study. Using answers from an experiment conducted on a Swedish sample where respondents ’ cognitive ability was measured and where they participate in a contingent valuation survey it is found that scale sensitivity is the strongest when respondents are asked about a smaller risk reduction first (“Bottom-up ” approach). This contradicts some previous evidence in the literature. It is also found that the respondents’ cognitive ability is correlated with their answers being line with theoretical predictions. The latter being important for the validity of the answers. Hence, the results of this paper suggest that scale sensitivity is related to the order of the questions and to respondents ’ cognitive ability

Imitation Learning for Vision-based Lane Keeping Assistance

This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.Comment: International Conference on Intelligent Transportation Systems (ITSC

Scale Sensitivity and Question Order in the Contingent Valuation Method

This study examines the effect on respondents' willingness to pay to reduce mortality risk by the order of the question in a stated preference study. Using answers from an experiment\ud conducted on a Swedish sample where respondents’ cognitive ability was measured and where they participate in a contingent valuation survey it is found that scale sensitivity is the strongest when respondents are asked about a smaller risk reduction first (“Bottom-up” approach). This contradicts some previous evidence in the literature. It is also found that the respondents’ cognitive ability is correlated with their answers being line with theoretical predictions. The latter being important for the validity of the answers. Hence, the results of this paper suggest that scale sensitivity is related to the order of the questions and to respondents’ cognitive ability

Site Communication in Direct Formation of H2O2 over Single-Atom Pd@Au Nanoparticles

Single atom alloy catalysts offer possibilities to obtain turnover frequencies and selectivities unattainable by their monometallic counterparts. One example is direct formation of H2O2 from O2 and H2 over Pd embedded in Au hosts. Here, a first-principles-based kinetic Monte Carlo approach is developed to investigate the catalytic performance of Pd embedded in Au nanoparticles in an aqueous solution. The simulations reveal an efficient site separation where Pd monomers act as active centers for H2 dissociation, whereas H2O2 is formed over undercoordinated Au sites. After dissociation, atomic H may undergo an exothermic redox reaction, forming a hydronium ion in the solution and a negative charge on the surface. H2O2 is preferably formed from reactions between dissolved H+ and oxygen species on the Au surface. The simulations show that tuning the nanoparticle composition and reaction conditions can enhance the selectivity toward H2O2. The outlined approach is general and applicable for a range of different hydrogenation reactions over single atom alloy nanoparticles

On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination

We consider the benefits of dream mechanisms – that is, the ability to simulate new experiences based on past ones – in a machine learning context. Specifically, we are interested in learning for artificial agents that act in the world, and operationalize “dreaming” as a mechanism by which such an agent can use its own model of the learning environment to generate new hypotheses and training data. We first show that it is not necessarily a given that such a data-hallucination process is useful, since it can easily lead to a training set dominated by spurious imagined data until an ill-defined convergence point is reached. We then analyse a notably successful implementation of a machine learning-based dreaming mechanism by Ha and Schmidhuber (Ha, D., & Schmidhuber, J. (2018). World models. arXiv e-prints, arXiv:1803.10122). On that basis, we then develop a general framework by which an agent can generate simulated data to learn from in a manner that is beneficial to the agent. This, we argue, then forms a general method for an operationalized dream-like mechanism. We finish by demonstrating the general conditions under which such mechanisms can be useful in machine learning, wherein the implicit simulator inference and extrapolation involved in dreaming act without reinforcing inference error even when inference is incomplete

Modeling and exploration of a reconfigurable architecture for digital holographic imaging

The use of coarse-grain reconfigurable architectures (CGRA) is a suitable alternative for hardware acceleration in many application areas, including digital holographic imaging. In this paper, we propose a CGRA-based system with an array of processing and memory cells, which communicate using a local and a global communication network, and a stream memory controller to manage data transfers to external memory. We present our SystemC-based exploration environment (SCENIC) and methodology used to construct and evaluate systems containing reconfigurable architectures. A case study illustrates the advantages with rapid system level exploration to find and solve bottlenecks in complex designs prior to RTL description

A hybrid interconnect network-on-chip and a transaction level modeling approach for reconfigurable computing

This paper presents a hybrid interconnect network consisting of a local network with dedicated wires and a global hierarchical network. A distributed memory approach enables the possibility to use generic memory banks as routing buffers, simplifies the implementation and reduces the area requirements of routers. A SystemC simulation environment (SCENIC) has been developed to simulate and instrument models, and to setup different topologies and scenarios. Modules are designed as transaction level models to improve design time and simulation speed