Blade Degradation Research Articles

Interaction of CsOH with structural steel 316L and simulated products of the steel-cladding reaction under assumed conditions relevant for the accident at the Fukushima Dai-Ichi Unit 2 and 3

A high radiation dose rate under the shield plugs of the Units 2 and 3 reactors at the Fukushima Dai-Ichi Nuclear Power Station (1F), raised an important question on volatile radioactive Cs migration, adsorption and retention on various structural materials during the accident. The high dose rate interferes with decommissioning work. The purpose of this study is to present basic knowledge on contamination mechanisms of a BWR reactor structural materials and post-accident debris by Cs compounds to help decommissioning companies consider the ways of decontamination and debris handling during 1F debris retrieval. Having this goal, a series of tests on CsOH contamination mechanism under assumed accident conditions for Units 2 and 3 was performed. The results may improve the accuracy of estimating the situation inside the reactor from the perspectives of accident analysis and materials’ properties. The test temperature and compounds were chosen based on the probable accident scenario. In particular, Cs adsorption was checked using preoxidized 316L stainless steel, a product of control blade degradation (steel-B4C eutectic) and a product of steel-channel box and cladding interaction (Zr-Fe based multiphase material). Penetration ability of Cs into the oxidized and metallic layers of materials was evaluated. CsOH most probable adsorption temperature range and new Cs containing phases’ formation temperature range were followed.

A New Concept of Sustainable Wind Turbine Blades: Bio-Inspired Design with Engineered Adhesives.

In this paper, a new concept of extra-durable and sustainable wind turbine blades is presented. The two critical materials science challenges of the development of wind energy now are the necessity to prevent the degradation of wind turbine blades for several decades, and, on the other side, to provide a solution for the recyclability and sustainability of blades. In preliminary studies by DTU Wind, it was demonstrated that practically all typical wind turbine blade degradation mechanisms (e.g., coating detachment, buckling, spar cap/shell adhesive joint degradation, trailing edge failure, etc.) have their roots in interface degradation. The concept presented in this work includes the development of bio-inspired dual-mechanism-based interface adhesives (combining mechanical interlocking of fibers and chemical adhesion), which ensures, on the one side, extra-strong attachment during the operation time, and on the other side, possible adhesive joint separation for re-use of the blade parts. The general approach and physical mechanisms of adhesive strengthening and separation are described.

A BWR control blade degradation observed in situ during a CLADS-MADE-02 test under Fukushima Dai-Ichi Unit 3 postulated conditions

ABSTRACT The paper summarizes the results of the control blade degradation test CLADS-MADE-02 performed in the Japan Atomic Energy Agency . The test focused on the beginning phase of the accident at Fukushima Dai-Ichi Nuclear Power Plant (1F) Unit 3. The investigation provided important data, especially on the temperature history, exhaust gas measurement, and in situ video of metallic debris formation and relocation to the colder elevations under the test scenario, which reproduced oxidizing conditions during the initial phase of the 1F Unit 3 reactor heat-up. Complementary post-test analysis by metallography, Raman spectroscopy, elemental scanning electron microscope/energy-dispersive X-ray spectrometry spectroscopy, and exhaust gas analysis provided additional data on the local materials interactions that gave insights on the melt progression scenarios. Based on the test results, some decommissioning-related conclusions concerning the formation of new B-rich phases containing Cr and Fe were made.

Analysis of leading edge erosion effects on turbulent flow over airfoils

The surface of wind turbine blades are prone to degradation due to exposure to the elements. Rain, hail, insects are among the many causes of turbine blade degradation or erosion. Surface degradation of the wind turbine blades leads to a reduction in the aerodynamic performance, resulting in power losses. The effect of surface degradation is studied by modeling the turbine blade as a rough surface. Surface roughness can be positive (insects or other foreign objects) or negative (erosion, delamination). The individual roughness elements are however very small and it is not always feasible to study the actual degraded surface. Thus various roughness models have been proposed in the literature which eliminate the need to accurately model the degraded surface by representing erosion with a virtual surface and modeling the effect of erosion on the flow quantities near the eroded surface. In this study, the reduction in performance of airfoils due to leading edge roughness is quantified. Different roughness models are investigated and evaluated against theoretical models. Additionally, the effect of roughness on different integral boundary layer quantities like displacement thickness, momentum thickness and skin friction are presented.

Failure mechanism of thermal barrier coating on the first stage turbine blade

One of the main components in a gas turbine for the power plant is the turbine blade. The turbine blade lifetime depends on materials, operation, and maintenance. A prominence reason for accelerate turbine blade degradation is caused by the failure of the thermal barrier coating (TBC). In the present work, we studied the failure of TBC on the 1st stage turbine blade. A scanning electron microscope, optical light microscope, and X-Ray diffraction were used for samples examination. The analysis results showed that the TBC of first stage turbine blades is a plasma-water type sprayed and consists of 7 wt. % yttria-stabilized zirconia (YSZ). Mechanism of separation in the TBC layer is caused by thermal fatigue due to thermal differences expansion between the TBC and the thermally grown oxide (TGO), thereby causing tensile stress and compression on the TBC. The pressure causes micro-cracks on the top coat lead to the separation of the TBC top coat.

Damage Mechanism Based Approach to the Structural Health Monitoring of Wind Turbine Blades

A damage mechanism based approach to the structural health monitoring of wind turbine blades is formulated. Typical physical mechanisms of wind turbine blade degradation, including surface erosion, adhesive fatigue, laminate cracking and in some cases compressive kinking and failure are reviewed. Examples of a local, damage mechanism based approach to the structural health monitoring of wind turbine blades are demonstrated, including the monitoring of leading edge erosion of wind turbine blades, adhesive bond failure, plydrop delamination, static and dynamic plydrop tests, and bolt and laminate fatigue. The examples demonstrate the possibilities of monitoring specific damage mechanisms, and specific localizations of wind turbine blades.

Comparison of the observed Fukushima Dai-Ichi Unit 2 debris with simulated debris from the CLADS-MADE-01 control blade degradation test

ABSTRACT In February 2019, TEPCO and TOSHIBA designed a robot that can handle the debris, performed a trial investigation of primary containment vessel (PCV) of the Fukushima Dai-Ichi (1F) Unit 2 and published a video. The video contained lots of valuable information which can be compared with results of BWR control blade degradation tests recently performed in Japan Atomic Energy Agency (JAEA). During the test under postulated 1F Unit 2 simulated conditions a complex behaviour of the test sample with the formation of mostly three types of debris was observed. Possible mechanisms governing the 1F Unit 2 debris formation and possible ways of their relocation are discussed.

Condition Monitoring for Single-Rotor Wind Turbine Load Sensors in the Full-Load Region

Incipient faults on blade load sensors can impede the sound performance of a wind turbine, leading to increasing loads over time and severe blade degradation. As such, knowledge of the blade load sensors’ health is essential for ensuring effective load reduction by means of individual pitch control. This paper presents a condition monitoring strategy for the blade load sensors based on estimation of the loads acting on the rotor blades in the full-load region. Fault detection is achieved via appropriate residual generators, the statistical properties of which are used to design change detectors robust against measurement noise and wind field stochasticity. Specifically, a Generalized Likelihood Ratio Test for the t-LocationScale distribution is developed for ensuring robust detection of sensor blade faults. The proposed method is evaluated in a high-fidelity simulator under non-uniform wind scenarios. The simulation results show that detection of multiplicative faults on the blade load sensors is achieved even in absence of knowledge of the local wind speed.

A novel scalable method for machine degradation assessment using deep convolutional neural network

Bandsaw machines are widely used in the rough machining stage to cut various materials into required dimensions. Deterioration on the blade, which is a critical component of the bandsaw machine, not only causes a waste of cutting material but also represents a major portion of the operation & maintenance cost for the machine user. Although non-high-end manufactures put as much emphasis on the accuracy of the cuts as high-end manufacturers, non-high-end bandsaw machine users are not as easily able to justify the high cost associated with the blade wear monitoring solution. Therefore, this paper proposes a methodology to develop a scalable blade degradation model that is suitable for massive deployment at an affordable cost. A 4-stage roadmap is proposed to provide step by step guidance in the development and deployment of the scalable blade degradation model. As the core issue of the roadmap, the degradation model development is solved by the proposed dual-phase modeling methodology. In phase I, a physics informed model (which relies on physical analysis to extract effective features) is established to generate a reliable health indicator (HI) to monitor the blade wear condition utilizing the critical vibration and acoustic signals. Phase II proposes to develop a deep convolutional neural network (DCNN) based surrogate model to replace the physics informed model. The DCNN based surrogate model will use only alternative low-cost sensor data. By eliminating the usage of the high-cost vibration and acoustic sensors, the developed surrogate model is expected to cost much less than the physics informed model. Finally, the effectiveness of the proposed methodology is validated using data from different bandsaw machines and blades, and the superior performance of the DCNN is observed as compared to traditional machine learning algorithms.

A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine

In the rough machining stage, band saw machines are widely used to cut various raw materials into the required dimensions. The replacement of blade due to the blade degradation accounts for a large part of the total cost of band saw machine users. Therefore, mitigating the blade degradation by dynamically optimizing the cutting parameters can produce great economic benefits and is also a good exploration for smart manufacturing. To achieve this goal, the Convolutional Neural Network (CNN) model is proposed to map the complicated relationship between the cutting parameters and blade degradation. Then a simulation technique is used to search the optimal cutting parameters based on the blade degradation estimation from the model output, which would help alleviate the blade degradation. The proposed optimization method is validated on the data collected during the real manufacturing process of band saw machines. The comparative results demonstrate that the obtained optimal cutting parameters can effectively extend the service life of the blade for a band saw machine.

Features of a control blade degradation observed in situ during severe accident conditions in boiling water reactors

ABSTRACT In the present paper, new results using in situ video are presented regarding boiling water reactor (BWR) control blade degradation up to 1750 K at the beginning of a nuclear severe accident. Energy-dispersive X-ray spectrometry (EDS) mapping indicated stratification of the absorber blade melt with formation of a chromium and boride-enriched layer. High-content-B- and C-containing material with increased melting temperature acted like a shielding and was found to prevent further relocation of control blade claddings. The interacted layers around the B4C-granules prevented direct steam attack of residual B4C. The results provide new insights for understanding of the absorber blade degradation mechanism under reducing conditions specific to Fukushima Dai-Ichi Unit 2 resulting from prolonged steam starvation.

Integration of Degradation Processes in a Strategic Offshore Wind Farm O&M Simulation Model

Decision support models for offshore wind farm operation and maintenance (O&M) are required to represent the failure behavior of wind turbine components. Detailed degradation modelling is already incorporated in models for specific components and applications. However, component degradation is only one of many effects that must be captured in high-level strategic decision support models that simulate entire wind farms. Thus, for practical applications, a trade-off is needed between detailed degradation modelling and the level of simplicity of input data representation. To this end, this paper discusses two alternative approaches for taking into account component degradation processes in strategic offshore wind farm O&M simulation models: (1) full integration of the degradation process in the O&M simulation model; and (2) loose integration where the degradation process is translated into simplified input to the O&M model. As a proof-of-concept, a Markov process for blade degradation has been considered. Simulations using the NOWIcob O&M model show that the difference between full and loose integration is small in terms of aggregated output parameters such as average wind turbine availability and O&M costs. Although loose integration models some effects less accurately than full integration, the former is more flexible and convenient, and the accuracy is for most purposes sufficient for such O&M models.

Regrowth and biofouling in two species of cultivated kelp in the Shetland Islands, UK

The meristem of the kelps Laminaria digitata and Saccharina latissima is located at the base of the blade, growth can therefore continue when the distal blade is lost due to erosion or harvesting. The aim of the study was to determine the regrowth potential of cultivated kelp in the Shetland Islands (UK) to assess feasibility of harvesting twice in one growing season. Laminaria digitata and S. latissima grown on longlines at sea were sampled between March and August, and harvested either at the stipe (whole) or 10 cm above the stipe-blade transition zone (partial) May–August. Image analysis was used to estimate blade length, width, area and fouled area. S. latissima increased in length more than L. digitata between March and August, and in August, while whole S. latissima experienced a net loss in length, partially cut blades had a net increase. Whole blades significantly increased in length for both species but only partially cut S. latissima significantly regrew. In late summer, severe biofouling by tunicates made up 12 and 27% of the biomass at two sites. Interestingly, S. latissima was less fouled than L. digitata. Consequently, S. latissima shows the greatest potential for the application of partial cutting to improve cultivated biomass yields. However, the period for regrowth is limited by low yields in early spring and blade degradation in late summer. In order to optimise biomass yields, further understanding is needed on the abiotic and biotic factors that control growth and biofouling on natural and farmed seaweed.

The influence of high temperature on DV-2 jet engine Ni-based superalloy turbine blade degradation

Blades and vanes made from Ni – and Co – base superalloys that are used in the hot section of all gas turbine engines are coated to enhance resistance to hot corrosion. The most widely used coatings are those based on the intermetallic compounds NiAl and CoAl, which are formed by diffusion interaction of aluminium with surfaces of the nickel and cobalt alloys, respectively. Simple aluminide coatings resist high – temperature oxidation by the formation of protective layer of alumina and can be used up to about 1 150°C. The coatings degrade by loss of aluminium due to spalling of oxides under thermal cycling conditions. HPT blades of DV – 2 jet engines are made from Ni – based superalloy ŽS6K. For improving alloy’s high temperature resistance are blades coated with Al – Si diffusion layer. Depending on jet engine flying regime an overcrossing of working temperature may occur. It may result into coarsening of the gamma prime particles, thus decreasing its strengthening effect. A real case study of DV-2 jet engine turbine blades after high temperature exposition is discussed in this article.

Fusion of Gaussian Process Kernel Regressors for Fault Prediction in Intelligent Energy Systems

Integration of energy systems with machine intelligence technologies advances the new generation of intelligent energy systems. One feature of intelligent energy systems is their ability to predict a future fault state (prognosis), and thus support control actions. This paper introduces a prognostic framework based on concepts originating from the machine learning universe and proceeds to assess the performance of the prognostics algorithms with statistical methods aiming to formulate a linear predictor whose coefficients are the solution of a multi-objective optimization problem. Prediction is achieved through independent Gaussian process kernel regressors put together as terms of a linear forecaster. In this novel framework the available data is used to train the regression models whose degradation predictions cover a predetermined time period and have the form of a predictive distribution whose mean and variance values are computed for future moments. Thus, given the observed data points one may search for the most probable values of other quantities of interest, or the values at different points from those measured. The feasibility of the cascading prognostics methodology is demonstrated via a turbine blade degradation example. This implementation is characterized by advantages that include the utilization of an optimization process that simultaneously determines the lowest possible values in a set of different statistical measures and the employment of a set of kernels for modeling various data features and capturing the system dynamics.

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

Fundamental experiments on phase stabilities of Fe–B–C ternary systems

To understanding the control blade degradation mechanism of a boiling-water reactor (BWR), a thermodynamic database for the fuel assembly materials is a useful tool. Although the iron, boron, and carbon ternary system is a dominant phase diagram, phase relation data are not sufficient for the region in which boron and carbon compositions are richer than the eutectic composition. The phase relations of three samples such as Fe0.68B0.06C0.26 (at%), Fe0.68B0.16C0.16 (at%), and Fe0.76B0.06C0.18 (at%) were analyzed by X-ray diffraction, scanning electron microscopy, and energy–dispersive X-ray spectrometry. The results indicate that the Fe3(B,C) phase exists only in the intermediate region at 1273 K and that the solidus temperature widely maintains at approximately 1400 K for all three samples; these results differ from the calculated data using the previous thermodynamic database. The difference might originate from the overestimation of the interaction parameter between boron and carbon in Fe3(B,C). Proper titling was performed using the present data.

Wind Turbine Condition Assessment Through Power Curve Copula Modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper, it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw, and pitch errors.

Probabilistic Kernel Approach to Online Monitoring of Nuclear Power Plants

Nuclear power plants are complex engineering systems comprised of many interacting and interdependent mechanical components whose failure might lead to degraded plant performance or unplanned shutdown with loss of power generation and negative economic impact. As a result, continuous component surveillance and accurate prediction of their failing points is necessary for their on-time replacement. In this paper, a probabilistic kernel approach for intelligent online monitoring of mechanical components is presented. Specifically, the probabilistic kernel notion of Gaussian processes (GPs) is applied to the distribution prediction of a component's degradation trend. The proposed method exploits the learning ability of a GP and updates its prediction using a feedback mechanism. The methodology is tested on actual turbine blade degradation data for a variety of topologies (i.e., kernels). The GP estimations are compared to those obtained with a nonprobabilistic, kernel-based machine learning algorithm, the support vector regression (SVR). The comparison outcome clearly demonstrates that GP prediction accuracy outperforms SVR in the majority of the cases while providing a predictive distribution instead of point estimates as SVR does.

Assessment of performance capability of turbine blades with protective coatings after overheating events

Rotor blades of the high-pressure aircraft turbine are the most heavily loaded components of the runner wheel at high-temperature conditions. Overheating is one of the severe degradation modes and depends on the degree and time of overheating. A rather simple form of a degradation parameter D was empirically proposed by the producer in order to assess the level of blade degradation after overheating. The parameter D was recently found to well reflect the level of degradation of protective surface coatings in terms of a reduction of its relative thickness. This paper shows that the parameter D can be physically understood in terms of the Hamiltonian action S and, according to the presented numerical analysis, it possesses the best form of all possible damage indicators of the same physical meaning.