Kaplan Turbine Blade Geometry Using CFD and Aquatic Plant Profiles
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Solution Overview
Problem
Existing Kaplan turbine blade designs lack efficiency due to the absence of research on aquatic plant-based designs, necessitating a novel approach for improved hydro power generation.
Innovation Solution
A system and method for designing advanced Kaplan turbine blades using an engineering equation solver (EES) for parameter calculation, 3D modeling, CFD analysis with the K-omega turbulent model, and machine learning for manufacturing, inspired by the shape of aquatic plants to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Kaplan turbine blade designs are used, then the design process is simple and well-established, but the efficiency and power output are insufficient
Solution Approach 1:
The patent applies parameter changes by systematically varying blade geometric parameters (blade angle, curvature, thickness distribution) to optimize turbine efficiency. The design process involves calculating and determining a set of parameters using engineering equation solver, then iteratively adjusting these parameters to achieve theoretical efficiencies up to 93.89% and power outputs exceeding 15 KW.
Solution Approach 2:
The patent implements dynamics by making the blade design adaptable through CFD analysis and machine learning approaches. The system allows for dynamic optimization of blade geometry based on performance data, enabling the design to evolve and improve efficiency through computational modeling and comparative study of different blade models.
2Productivity
If aquatic plant-inspired blade designs are implemented, then efficiency and power output increase significantly, but the design and analysis complexity increases
Solution Approach 1:
The patent replaces traditional mechanical trial-and-error design methods with computational systems. It uses engineering equation solver for parameter calculation, CFD analysis for performance prediction, and machine learning for manufacturing optimization, substituting physical experimentation with virtual modeling to achieve efficiencies up to 93.89% while managing design complexity.
Solution Approach 2:
The patent introduces CFD analysis as an intermediary between blade design and performance evaluation. The K-omega turbulent model with Y+ of 1 serves as a mediator to accurately predict water flow behavior and rotational pressure on blades, enabling virtual testing and optimization before manufacturing, thus bridging the gap between design complexity and efficiency gains.
3Measurement precision
If CFD analysis with K-omega turbulent model is used, then accurate performance prediction is achieved, but computational time and resources increase
Solution Approach 1:
The patent applies preliminary action by performing CFD analysis with pre-configured K-omega turbulent model and Y+ of 1 settings before manufacturing. The system calculates and determines all necessary parameters in advance using engineering equation solver, preparing optimized blade designs beforehand to minimize actual computational time during implementation while maintaining measurement precision for performance prediction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The aquatic plant-inspired blade design achieves higher efficiency and power output compared to traditional models, with theoretical efficiencies up to 93.89% and power outputs exceeding 15 KW, demonstrating a cost-effective and expeditious design process.
Implementation Method 1
CFD analysis of the turbine models on based on the K-omega turbulent model with a Y+ of 1, wherein the turbulent model is used to ensure the near wall function of water and the rotational pressure applied on the blades
Data Source
AI summary
The system for designing advanced Kaplan turbine-based on advanced blade design of a hydro powered turbine comprises an EES for calculating and determining a set of parameters involved in the designing of Kaplan turbine blade; a designing user interface for designing a 3d-model of the Kaplan turbine blade; an analyzing unit for CFD analysis of the turbine models on based on the K-omega turbulent model with a Y+ of 1, wherein the turbulent model is used to ensure the near wall function of water and the rotational pressure applied on the blades thereby generating results of the analysis using CFD post and plotted on a table for the comparative study of the blade models; and a manufacturing unit for manufacturing Kaplan turbine blade based on comparative study of the blade models using a machine learning approach.