The goal of this category is to provide technical support for TCAE users to share their knowledge and experience to help others troubleshoot technical issues, improve the performance of their simulations, and learn from one another. This category is focused on providing technical support and guidance exclusively for TCAE. Members can ask questions, share their own solutions to common problems, and collaborate to improve the overall performance and efficiency of TCAE. The category is open to anyone who is interested in TCAE, regardless of their level of expertise.

Propeller DJI 9450, Phantom 3 drone. CFD + CAA simulation by TCAE. Pressure distribution.

#propeller #acoustics #Simulation #CAE #CFD #FEA #FSI #Optimization #CAA #HPC #engineering

https://www.linkedin.com/feed/update/urn:li:activity:6993850175274295298

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.

Magnus Effect - A phenomenon that is commonly associated with a spinning object moving through the air. Compared to the non-spinning object, the spinning one produces a significant lift force. We tested this phenomenon in the study of the aerodynamics of rotating and non-rotating balls with TCFD back in 2016. 

https://youtu.be/MvIhJhhW6jc

#Simulation #CAE #CFD #FEA #FSI #CAA #Optimization #HPC #engineering #turbomachinery

An exhaust manifold is an important part of every combustion engine that collects the exhaust gases from multiple cylinders into one pipe. The gas flow in the manifold can be extensively analyzed and optimized by CFD analysis. Have a look at an example of an aerodynamic analysis of exhaust manifold simulation by TCFD. https://youtu.be/EIn2oUIX6VM

Parametric pipe A tutorial example of CFD simulation-driven optimization. The tutorial geometry is a simple tube curved by 180 degrees. This pipe is fully parametric and it’s built with help of a python script. There were chosen three locations with three variable parameters (pipe radius). The optimization function is the efficiency based on the fluid flow pressure loss. The optimization loop iterates to find the best possible set of parameters. By TCAE.

https://www.cfdsupport.com/img/TCAE-Parametric-Pipe-Optimization-2-400x.gif       https://www.cfdsupport.com/img/TCAE-Parametric-Pipe-Optimization-2-LIC.gif  

https://youtu.be/B27iOr5Qxzg

https://youtu.be/bE4lHDOpsps

As you know, at CFDSUPPORT, we are obsessed with the accuracy of CFD results. Accuracy is our highest priority. We believe that every successful engineering project is a result of focus, skills, experience, patience, and dedication (not random actions in a rush). We constantly keep tracking the accuracy and create comparisons of CFD with real physical measurements. Have a look at this Centrifugal Compressor Benchmark. This project was undertaken jointly by CFDSUPPORT in collaboration with CZ a.s. (compressor manufacturer). The aim of the benchmark validation was to evaluate the TCFD software and compare its simulation results with the compressor measurement data.