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Because of its minor environmental impact, electricity generation using wind power is getting remarkable. The further growth of the wind industry depends on technological solutions to the challenges in production and construction of the turbines. Wind turbine tower vibrations, which limit power generation efficiency and cause fatigue problems with high maintenance costs, count as one of the main structural difficulties in the wind energy sector. To mitigate tower vibrations auxiliary measures are necessary. The effectiveness of tuned mass damper is verified by means of a numeric study on a 5 MW onshore reference wind turbine. Hereby, also seismic-induced vibrations and soil–structure interaction are considered. Acquired results show that tuned mass damper can effectively reduce resonant tower vibrations and improve the fatigue life of wind turbines. This chapter is also concerned with tuned liquid column damper and a semiactive application of it. Due to its geometric versatility and low prime costs, tuned liquid column dampers are a good alternative to other damping measures, in particular for slender structures like wind turbines.
Textile reinforced concrete. Part I: Process model for collaborative research and development
(2003)
A concept for the analysis and optimal design of reinforced concrete structures is described. It is based on a nonlinear optimization algorithm and a finite element program for linear and nonlinear analysis of structures. With the aim of minimal cost design a two stage optimization using efficient gradient algorithm is developed. The optimization problems on global (structural) and local (crosssectional) level are formulated. A parallelization concept for solving the two stage optimization problem in minimal time is presented. Examples are included to illustrate the practical use and the effectively of the parallelization in the area of engineering design.
Lately there has been an increasing concern about uranium toxicity in some districts of Punjab State located in the North Western part of India after the publication of a report (Blaurock-Busch et al. 2010) which showed that the concentration of uranium in hair and urine of children suffering from physical deformities, neurological and mental disorder from Malwa region (Fig. 1) of Punjab State was manifold higher than the reference ranges. A train which connects the affected region with the nearby city of Bikaner which has a Cancer Hospital has been nicknamed as Cancer Express due to the frenzy generated on account of uranium related toxicity.
Following earlier studies, we present forward and inverse simulations of heat and fluid transport of the upper crust using a local 3-D model of the Kola area. We provide best estimates for palaeotemperatures and permeabilities, their errors and their dependencies. Our results allow discriminating between the two mentioned processes to a certain extent, partly resolving the non-uniqueness of the problem. We find clear indications for a significant contribution of advective heat transport, which, in turn, imply only slightly lower ground surface temperatures during the last glacial maximum relative to the present value. These findings are consistent with the general background knowledge of (i) the fracture zones and the corresponding fluid movements in the bedrock and (ii) the glacial history of the Kola area.
Modeling contribution to risk assessment of thermal production power for geothermal reservoirs
(2013)
The proposed Den Haag Zuidwest district heating system of the city of The Hague consists of a deep doublet in a Jurassic sandstone layer that is designed for a production temperature of 75 °C and a reinjection temperature of 40 °C at a flow rate of 150 m3 h−1. The prediction of reservoir temperature and production behavior is crucial for success of the proposed geothermal doublet. This work presents the results of a study of the important geothermal and geohydrological issues for the doublet design. In the first phase of the study, the influences of the three-dimensional (3D) structures of anticlines and synclines on the temperature field were examined. A comprehensive petrophysical investigation was performed to build a large scale 3D-model of the reservoir. Several bottomhole temperatures (BHTs), as well as petrophysical logs were used to calibrate the model using thermal conductivity measurements on 50 samples from boreholes in different lithological units in the study area. Profiles and cross sections extracted from the calculated temperature field were used to study the temperature in the surrounding areas of the planned doublet. In the second phase of the project, a detailed 3D numerical reservoir model was set up, with the aim of predicting the evolution of the producer and injector temperatures, and the extent of the cooled area around the injector. The temperature model from the first phase provided the boundary conditions for the reservoir model. Hydraulic parameters for the target horizons, such as porosity and permeability, were taken from data available from the nearby exploration wells. The simulation results are encouraging as no significant thermal breakthrough is predicted. For the originally planned location of the producer, the extracted water temperature is predicted to be around 79 °C, with an almost negligible cooling in the first 50 years of production. When the producer is located shallower parts of the reservoir, the yield water temperatures is lower, starting at ≈76 °C and decreasing to ≈74 °C after 50 years of operation. This comparatively larger decrease in temperature with time is caused by the structural feature of the reservoir, namely a higher dip causes the cooler water to easily move downward. In view of the poor reservoir data, the reservoir simulation model is constructed to allow iterative updates using data assimilation during planned drilling, testing, and production phases. Measurements during an 8 h pumping test carried out in late 2010 suggest that a flow rate of 150 m3 h−1 is achievable. Fluid temperatures of 76.5 °C were measured, which is very close to the predicted value.
Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.