Study of high resistance inorganic coatings on graphite fibers

Coatings made of boron, silicon carbide, silica, and silica-like materials were studied to determine their ability to increase resistance of graphite fibers. The most promising results were attained by chemical vapor depositing silicon carbide on graphite fiber followed by oxidation, and drawing graphite fiber through ethyl silicate followed by appropriate heat treatments. In the silicon carbide coating studies, no degradation of the graphite fibers was observed and resistance values as high as three orders of magnitude higher than that of the uncoated fiber was attained. The strength of a composite fabricated from the coated fiber had a strength which compared favorably with those of composites prepared from uncoated fiber. For the silica-like coated fiber prepared by drawing the graphite fiber through an ethyl silicate solution followed by heating, coated fiber resistances about an order of magnitude greater than that of the uncoated fiber were attained. Composites prepared using these fibers had flexural strengths comparable with those prepared using uncoated fibers, but the shear strengths were lower

Coatings for graphite fibers

Graphite fibers released from composites during burning or an explosion caused shorting of electrical and electronic equipment. Silicon carbide, silica, silicon nitride and boron nitride were coated on graphite fibers to increase their electrical resistances. Resistances as high as three orders of magnitude higher than uncoated fiber were attained without any significant degradation of the substrate fiber. An organo-silicone approach to produce coated fibers with high electrical resistance was also used. Celion 6000 graphite fibers were coated with an organo-silicone compound, followed by hydrolysis and pyrolysis of the coating to a silica-like material. The shear and flexural strengths of composites made from high electrically resistant fibers were considerably lower than the shear and flexural strengths of composites made from the lower electrically resistant fibers. The lower shear strengths of the composites indicated that the coatings on these fibers were weaker than the coating on the fibers which were pyrolyzed at higher temperature

Ultimate Limit State Fragility of Offshore Wind Turbines on Monopile Foundations

Assessing the risk posed by extreme natural events to the failure of offshore wind turbines (OWTs) is a challenging task. Highly stochastic environmental conditions represent the main source of variable loading; consequently, a high level of uncertainty is associated with assessing the structural demand on OWT structures. However, failure of any of the primary structural components implies both complete loss of the OWT and loss of earnings associated with production stoppage (i.e., business interruption). In this paper, we propose the use of the Catastrophe (CAT) Risk Modelling approach to assess the structural risk posed by extreme weather conditions to OWTs. To help achieving this, we develop fragility curves – a crucial element of any CAT models – for OWTs on monopile foundations. Fragility functions express the likelihood of different levels of damage (or damage states) sustained by a given asset over a range of hazard intensities. We compare the effect of modelling and analysis decisions on the fragility curves, highlighting how different procedures could affect the estimated probability of failure. We apply the proposed framework to two case-study locations, one in the Baltic Sea and one in the North Sea

Human-centric light sensing and estimation from RGBD images: the invisible light switch

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices

Gaussian process regression for fatigue reliability analysis of offshore wind turbines

The fatigue limit state (FLS) often drives the design of offshore wind turbine (OWT) substructures in European waters. Assessing fatigue damage over the intended design life of an OWT is computationally expensive, primarily as dynamic structural analyses have to be run for a large number of stochastic wind and wave loading conditions. This makes structural reliability assessment for the FLS a challenging task. In addition to evaluating load-induced fatigue damage, simulation-based structural reliability analysis also requires sampling of random variables that model uncertainties in the capacity of OWT structural components. To this aim, we develop and validate a computational framework for OWT fatigue reliability analysis that relies on Gaussian process (GP) regression to build surrogate models of load-induced fatigue damage. We demonstrate that the proposed approach can reduce the computational effort required to evaluate FLS reliability with high accuracy through application to three plausible offshore wind farm sites in Europe. The sensitivity of various goodness-of-fit metrics to different model assumptions is investigated to further reduce the computational effort required to perform GP regression/predictions. The results from this study can provide guidance for practical applications of the proposed framework in OWT projects

Impact of climate-change scenarios on offshore wind turbine structural performance

Offshore wind turbines (OWTs) must be sufficiently safe and resilient to withstand windstorms over an operational life of 20–25 years in an aggressive marine environment. Current performance-based assessment methods for OWTs often neglect structural failure and focus on equipment failure only, which can be assessed using existing empirical databases. This study uses a simulation-based approach to assess various performance metrics associated with offshore wind infrastructure exposed to operational wind and wave conditions. Surrogate modelling is used to predict structural failure due to fatigue in a computationally efficient manner. The proposed surrogate model is based on Gaussian process regression and allows one to run structural simulations at a small training sample of wind and wave conditions and emulates the response at combinations where the OWT was not explicitly assessed. This results in an integrated probabilistic performance-based assessment framework for OWTs that considers both structural and non-structural (equipment) components. In particular, the proposed framework is used to evaluate the potential impact of climate-change scenarios on various OWT performance metrics, namely, fatigue damage, fatigue reliability and, ultimately, financial losses (cost of direct damage) for a case study OWT. This is compared to the change in revenue resulting from power production to understand which is more sensitive to climate change. Both fatigue damage and structural safety are found to be sensitive to changes in the site environmental conditions. However, as financial losses additionally depend on non-structural components - which are typically characterised by much higher failure rates - they are found to be less sensitive to the considered climate-change scenarios

A Bayesian model for wind farm capacity factors

Capacity factors are an important performance metric for offshore wind energy projects as they indicate how efficiently a given project generates electricity. Given the intermittent nature of the wind resource, there is substantial variability between observed capacity factors seasonally and between years. However, little work has focused on extracting trends in the variable wind farm energy generation data. This paper proposes applying hierarchical Bayesian techniques to historical capacity factors to enable the prediction of capacity factor distributions. The proposed model relies on data from UK offshore wind farms, the most developed market for offshore wind energy, but is equally applicable to other countries. The resulting capacity factor distributions highlight a substantial variability in capacity factors both when modelled as yearly and monthly (which accounts for seasonality). The model shows that newer wind farms have higher capacity factors than older farms, with an improvement of approximately 20%. It is also demonstrates that capacity factor variability has a smaller impact on levelized cost of energy estimates. The results from this study can be used both to predict capacity factors for individual wind farms or to make predictions for a generic wind farm

A framework for assessing structural resilience of offshore wind turbines

The concept of resilience provides a useful framework for considering an offshore wind turbine (OWT) as a system of integrated structural and mechanical components. This paper proposes quantifying economic losses associated with failure of an OWT using a catastrophe (CAT) risk modelling framework. Providing a first step towards evaluating offshore wind farm (OWF) resilience. A site-specific assessment of structural fragility is developed and then combined with empirical mechanical and electrical component failure rates to assess losses. The results from a case-study application indicate that failure of the structure plays a major role on the overall risk profile of an OWF, but depends on the severity of the site environmental conditions

Fatigue reliability of offshore wind turbines using Gaussian processes

The fatigue limit state (FLS) often drives the design of offshore wind turbine (OWT) substructures. Numerical assessment of fatigue damage over the life of a structure is computationally expensive, due to the need for time-history simulation of a large number of environmental conditions. This makes structural reliability for FLS a challenging task as it also requires numerical sampling of random variables to model uncertainty in the estimation of fatigue damage. This paper proposes using Gaussian process regression to build surrogate models for fatigue damage caused by different environmental conditions. A case study demonstrates how the proposed approach reduces the computational effort required to evaluate the FLS. Finally, a structural reliability calculation using the surrogate model highlights the large scatter in fatigue life prediction due to parameter uncertainty

A probabilistic framework for offshore wind turbine loss assessment

During their operational life, offshore wind turbines (OWTs) may be exposed to severe storms, resulting in extreme wind and wave loading acting on the OWT structure (and substructure). This paper proposes quantifying financial losses associated with an OWT exposed to extreme wind and waves using a probabilistic risk modelling framework, as a first step towards evaluating offshore wind farm (OWF) resilience. The proposed modelling framework includes a number of novel elements: 1) the development of site-specific fragility relationships (i.e., likelihood of different levels of damage experienced by an OWT over a range of hazard intensities), properly accounting for uncertainties in both structural capacity and demands; 2) the implementation of a closed-form technique, based on a combinatorial system reliability approach, to assess failure consequences (e.g., financial loss) for both structural and non-structural components; 3) a coherent treatment of epistemic uncertainties across the framework (e.g., sampling variability in fragility estimation), providing loss results accounting for uncertainty of estimation. These aspects can allow for more informative comparisons of various design solutions in terms of structural fragility and risk and/or for an improved evaluation of probabilistic losses for decision making. An illustrative application to two case-study sites is presented as a simplified walk-through of the calculation steps in the proposed framework, discussing possible outcomes. For instance, the results from the illustrative application indicate that the structural components play an important role in the overall risk profile of an OWT, but this depends on the site-specific wind and wave conditions. The calculation of losses provides a foundation from which a more detailed assessment of OWT and OWF resilience could be developed