Nrel 5 Mw Wind Turbine : Cfd Analysis With Validation
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.78 GB | Duration: 3h 46m
Expert CFD Analysis of Wind Turbines: Advanced Simulation Techniques and Validation with ANSYS Fluent
What you'll learnStudents will grasp the fundamentals of CFD modeling specifically for wind turbines.
Participants will create high-quality CFD meshes that accurately resolve boundary layers and provide fine detail in critical areas.
Students will set up and execute simulations in Fluent, optimizing settings for wind turbine applications.
Participants will perform post-processing of results in both Fluent and CFD Post, validating their findings against experimental data from NREL
RequirementsFundamental knowledge of wind turbines and fluid dynamics
Practical experience with ANSYS CFD software (such as Fluent)
A computer with at least 16 GB of RAM; 32 GB is preferred, equipped with an i5 or i7 processor (10th generation or newer)
Windows 10 Professional operating system
ANSYS version 2024 R1 or newer
DescriptionThis course offers a comprehensive and in-depth exploration of computational fluid dynamics (CFD) as it applies to the analysis of a 5 MW wind turbine, a vital component in harnessing wind power for sustainable electricity generation. In 2023, wind energy contributed approximately 7.81% of global electricity production, underscoring the critical need for efficient turbine design in today's renewable energy landscape. Using the Dutch Offshore Wind Energy Converter (DOWEC) 6 MW turbine as a reference, students will learn the complete CFD process, encompassing everything from geometry preparation to final results validation.Participants will start by defining the computational domain through the import of their SolidWorks models into SpaceClaim. They will utilize a watertight workflow to generate a high-quality mesh in Fluent Meshing, which is essential for accurate simulations. This foundational work will prepare them for conducting detailed simulations in ANSYS Fluent, where they will analyze turbine performance under specific operational conditions, including an inlet velocity of 11.4 m/s and a rotational speed of 12.1 RPM. With an impressive simulation error margin of only 0.3%, students will cultivate valuable skills in result validation and learn how to interpret aerodynamic performance metrics effectively.By the end of the course, students will have mastered advanced CFD techniques specifically tailored for large wind turbines. This hands-on experience will empower them to tackle real-world challenges in turbine design and optimization, equipping them with the knowledge and skills necessary to contribute meaningfully to the renewable energy sector. Graduates of this course will be well-prepared to pursue impactful careers in engineering, research, and innovation in the field of sustainable energy.
OverviewSection 1: Introduction
Lecture 1 Introduction
Lecture 2 Geometry Description
Lecture 3 Domain size and CFD modeling discussion for wind turbines
Lecture 4 Domain creation in spaceclaim
Lecture 5 Creating body on influence for mesh refinement in wake region
Lecture 6 Meshing in Fluent Meshing using watertight work-flow
Lecture 7 Running simulation and expected results
Lecture 8 Mesh improvement, advanced level commands and improved results
Lecture 9 Y+ adaptation
Lecture 10 Results postprocessing in Fluent
Lecture 11 Results post processing in CFD-Post
This course is designed for engineering students, recent graduates, and professionals in the fields of mechanical, aerospace, or renewable energy engineering who are interested in enhancing their skills in computational fluid dynamics (CFD) specifically for wind turbine analysis. It is ideal for those looking to deepen their understanding of turbine design and optimization, as well as for researchers and engineers involved in renewable energy projects. Additionally, anyone aiming to advance their expertise in CFD tools like ANSYS Fluent will find this course beneficial.
Screenshots
rapidgator.net:
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