Hello everyone,
I'm having a problem with a value (Cp: specific heat) that I want to set as default in all my TCAE projects. For that I modified the file TCAE-23.03v1/TCAE/etc/CAEProcessorDefaults.tcae by adding
Cp "1800_J/(kg.K)"
When I create a new TCAE project, the value of Cp is well defined at 1800 J/kg/K. However, it seems that when through the GUI I set the value to 0

and that I save, restore then check, no error is reported and I can run the simulation without any problem. That's when I looked at the TCAEproject/setup file and I was surprised to see that the heat capacity value was changed to
Cp "1004_J/(kg.K)"
This value corresponds to the air, originally set by default in the CAEProcessorDefaults.tcae configuration file.

So why my value that I set to 1800 J/kg/K was not returned ?

am Propeller CFD Benchmark is a set of test cases for evaluating the accuracy and performance of computational fluid dynamics (CFD) codes. It involves simulating the flow around a model of a ship propeller, which is a challenging and complex problem due to the highly unsteady and three-dimensional flow around the blade sections. The benchmark provides a set of reference solutions and metrics for the comparison and validation of CFD simulations. It is commonly used in the CFD community to evaluate new numerical methods and algorithms, and to compare different CFD codes and computing platforms.

CFDSUPPORT did the Potsdam propeller CFD Benchmark including FEA + FSI. If you are wondering how accurate the CFD simulation can be in comparison with the real physical measurement, take a look at th

A forum category to discuss and exchange information, ideas, and knowledge related to the context of FEA and FSI. The goal of this category is to serve as a community of users, developers, and experts who interact and collaborate on various aspects of the software, such as usage, development, bug fixing, and feature requests. Joining this discussion group is a great way to learn from others and stay updated on the latest developments in open-source FEA and FSI. Specifically using Calculix but not exclusively.

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.

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