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(1 - 3 of 3)
- Title
- Structural Condition Assessment for Wind Turbine Towers
- Creator
- Zahraee, Afshin
- Date
- 2019
- Description
-
Wind-based energy generation has special priority in efforts related to global sustainability. Based on this priority and the desire for...
Show moreWind-based energy generation has special priority in efforts related to global sustainability. Based on this priority and the desire for increase in electricity generation, the size of wind turbines has been tremendously increased in recent years. Moreover, larger wind turbines have access to more stable wind speeds which assists in electricity generation consistency. However, larger wind turbines are more prone to exhibit structural failure due to the increase of size as well as presence of complexities in the structure and wind load interaction. As such, condition monitoring and fault diagnosis of wind turbines are crucial in their sustainable operation. In this work, a new framework for condition assessment of wind turbine towers is developed. This framework enhances the ability to assess the structural condition of in-service wind turbine towers. Using this framework: 1) the wind data for the wind turbine location is collected, 2) a series of numerical modeling and analysis for the wind turbine tower for various wind velocities are performed to obtain the maximum induced stresses and their corresponding critical fatigue components (hot spots), and 3) fatigue analysis is performed leading to prediction for the remaining life of the wind turbine tower. To illustrate the capability of the present method, a case study is performed on an existing wind turbine. The obtained analytical results are compared and verified by the original design parameters. The results obtained for life prediction of the wind turbine tower correlate with life predictions of other existing wind turbine towers. It is anticipated that application of this framework for existing and future wind turbines will enhance their inspection planning as well as offer a more cost-effective process for repair and rehabilitation of wind turbine towers. This will ultimately increase the overall safety of wind turbine systems and enhance their reliability of performance.Keywords: Wind Turbine Tower, Condition Assessment, Life Prediction.
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- Title
- An Experimental Investigation of Single Jet Heat Transfer with Surrounding Microjets
- Creator
- Ma, Weicong
- Date
- 2019
- Description
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An experimental investigation of a single main jet with surrounding microjets impinging on a flat heated surface was performed to understand...
Show moreAn experimental investigation of a single main jet with surrounding microjets impinging on a flat heated surface was performed to understand the role of the higher-speed microjets on the surface heat transfer. Eight microjets 45-degrees apart were fabricated on a circular disk mounted at the exit of the main jet axisymmetric. Heat transfer enhancement on the flat surface was evaluated by comparison with the results of a baseline single round jet with the same flow rate. The average Nusselt Number and the local Nusselt number in the radial direction are reported as functions of dimensionless nozzle-to-plate distance, dimensionless radial distance, and dimensionless mass flow rate ratio. Local Nusselt number contours are plotted as a function of radial position. The area-averaged Nusselt number and local Nusselt number beyond the near-field impingement jet region increases monotonically with increasing mass flow rate ratio and decreasing of nozzle-to-target distance. The local Nusselt number at the stagnation region shows a more complex behavior with the mass flow rate ratio and nozzle-to-target distance.
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- Title
- DEEP LEARNING IN ENGINEERING MECHANICS: WAVE PROPAGATION AND DYNAMICS IMPLEMENTATIONS
- Creator
- Finol Berrueta, David
- Date
- 2019
- Description
-
With the advent of Artificial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the...
Show moreWith the advent of Artificial Intelligence research in the 1960s, the need for intelligent systems that are able to truly comprehend the physical world around them became relevant. Significant milestones in the realm of machine learning and, in particular, deep learning during the past decade have led to advanced data-driven models that are able to approximate complex functions from pure observations. When it comes to the application of physics-based scenarios, the vast majority of these models rely on statistical and optimization constructs, leaving minimal room in their development for the physics-driven frameworks that more traditional engineering and science fields have been developing for centuries. On the other hand, the more traditional engineering fields, such as mechanics, have evolved on a different set of modeling tools that are mostly based on physics driven assumptions and equations, typically aided by statistical tools for uncertainty handling. Deep learning models can provide significant implementation advantages in commercial systems over traditional engineering modeling tools in the current economies of scale, but they tend to lack the strong reliability their counterparts naturally allow. The work presented in this thesis is aimed at assessing the potential of deep learning tools, such as Convolutional Neural Networks and Long Short-Term Memory Networks, as data-driven models in engineering mechanics, with a major focus on vibration problems. In particular, two implementation cases are presented: a data driven surrogate model to a Phononic eigenvalue problem, and a physics-learning model in rigid-body dynamics scenario. Through the applications presented, this work that shows select deep learning architectures can appropriately approximate complex functions found in engineering mechanics from a system’s time history or state and generalize to set expectations outside training domains. In spatio-temporal systems, it is also that shown local learning windows along space and time can provide improved model reliability in their approximation and generalization performance
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