Axial Fan Simulation-driven Optimization workflow. By TCAE.

YouTube video:

Full Case Study (download available):

https://www.cfdsupport.com/axial-fan-simulation-driven-optimization.html

Optimization

Axial Fan Simulation-driven Optimization workflow. By TCAE.

YouTube video:

Full Case Study (download available):

https://www.cfdsupport.com/axial-fan-simulation-driven-optimization.html

Optimization
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TOPT - Free RPM for Kaplan Turbine

- Post By Tomáš Krejča
- 1 year ago
- Post Type Public

In CFD we generally have a problem when we encounter something that is supposed to move on its "free will". One of these problems is an unknown rotation speed of a turbomachinery rotor. Common practice is that the user prescribes flow boundary conditions for one specific case or does a full spectrum of operational conditions including rotation speed. But what if he knows an input aerodynamic or shaft torque and wants to obtain a stable rotation speed (with no acceleration)?

One way is to use the TOPT algorithm where you look for a minimum of optimization function prescribed as (target_torque - calculated_torque)^2.

First I calculated a torque for 187 RPM. Then I set this torque to our optimization function and ran TOPT with RPMs as a parameter.

After several runs, the RPMs started to converge around the value of 187 as they should. The DIRECT algorithm was still trying other options, but got to the minimum of our function relatively quickly.
Optimization

This category is dedicated to Optimization in Engineering Simulations. We discuss here optimization methods, tools, and case studies.

CFD (Computational Fluid Dynamics), FEA (Finite Element Analysis), and FSI (Fluid-Structure Interaction) simulations are used to analyze and predict the behavior of fluid flow, heat transfer, and structural mechanics in various engineering applications. Optimization is the process of finding the best solution to a problem by adjusting various input parameters. Optimization can be applied to CFD, FEA, and FSI simulations to improve the accuracy and efficiency of the simulation results. For example, optimization algorithms can be used to adjust mesh size, boundary conditions, and material properties to improve the accuracy of the simulation results, or to minimize the computational cost of the simulation.

Parametric optimization is a specific type of optimization in which the input parameters of a CFD, FEA, or FSI simulation are varied systematically to find the optimal solution. In parametric optimization, a set of input parameters is defined as the design variables, and a set of performance criteria is defined as the objective function. The optimization algorithm then searches for the combination of input parameters that minimizes or maximizes the objective function.

For example, in a CFD simulation of fluid flow through a pipe, the design variables might include the pipe diameter and the roughness of the pipe walls, while the objective function might be the pressure drop across the pipe. The optimization algorithm would then search for the combination of pipe diameter and roughness that minimizes the pressure drop.

Similarly, in an FEA simulation of a structure, the design variables might include the material properties and dimensions of the structure, while the objective function might be the stress or deflection of the structure. The optimization algorithm would then search for the combination of material properties and dimensions that minimizes the stress or deflection.

In an FSI simulation, the design variables are the parameters that affect the fluid and structure behavior, the objective function can be the displacement of the structure or pressure of the fluid.

Parametric optimization can be a powerful tool for designing and optimizing engineering systems, as it allows engineers to quickly and efficiently explore the design space and find the best solutions to their problems.