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Tutorials
Tutorials show you how to use Simcenter STAR-CCM+ for various applications in a step-by-step format with recommendations for setup, initialization and steps of the solution process specific to the application. Macro and simulation files are available for download for a large proportion of cases.
Design Exploration
The tutorials in this set illustrate various features for running design exploration studies in Design Manager.
Adjoint Topology Optimization: Flow through a U-Bend
Simcenter STAR-CCM+ provides adjoint topology optimization as a method for finding optimized designs for complex flow problems. Topology optimization can aid in the design of, for example, components found in aeroplanes or ground transportation vehicles such as air ducts by improving performance and efficiency.
Analyzing Topology Optimization Results
You can follow the progress of the solution using the material indicator scene that you created previously.

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Tutorials
Tutorials show you how to use Simcenter STAR-CCM+ for various applications in a step-by-step format with recommendations for setup, initialization and steps of the solution process specific to the application. Macro and simulation files are available for download for a large proportion of cases.
- Using Tutorial Macros and Files
  Macros, input files, and final simulation files for a range of tutorials are provided as an optional download package on the Support Center website. These macros and final simulation files are provided as an aid to the written tutorials, so that you can check your final results against the downloaded files, or against a simulation that is built and run using the macros.
- Introduction
  Welcome to the Simcenter STAR-CCM+ introductory tutorial. In this tutorial, you explore the important concepts and workflow. Complete this tutorial before attempting any others.
- Foundation Tutorials
  The foundation tutorials showcase the major features of Simcenter STAR-CCM+ in a series of short tutorials.
- Geometry
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for creating and working with parts and 3D-CAD.
- Mesh
  The tutorials in this set illustrate various STAR-CCM+ features for building CFD meshes.
- Incompressible Flow
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for incompressible fluid flows as well as porosity and solution recording
- Compressible Flow
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for compressible fluid flows as well as harmonic balance.
- Heat Transfer and Radiation
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for heat transfer, radiation, and thermal comfort.
- Multiphase Flow
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for simulating multiphase fluid flow problems
- Discrete Element Method
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for simulating Discrete Element Method problems
- Motion
  The tutorials in this set illustrate various STAR-CCM+ features for simulating problems with moving geometries and meshes, dynamic fluid body interaction, and rigid body motion:
- Reacting Flow
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for simulating reacting flows such as combustion.
- Solid Stress
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for computing deformation, strain, and stresses in solid regions. They also show how such computations can be coupled to the fluid behavior in an analysis of fluid-structure interaction.
- Aeroacoustics
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for solving aeroacoustic simulations.
- Electromagnetism
  The following tutorials illustrate features for solving problems that involve electromagnetic fields.
- Electrochemistry
  The following tutorial demonstrates chemical reactions induced by an electrical current.
- Battery
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for setting up a battery model:
- Automation
  The tutorials in this set illustrate various Simcenter STAR-CCM+ automation and macro features.
- Design Exploration
  The tutorials in this set illustrate various features for running design exploration studies in Design Manager.
  - Adjoint Shape Optimization: Mesh Sensitivity for Dual Element Wing
    A typical application of an adjoint flow analysis is to use the computed gradients in a shape optimization study with respect to a chosen cost function. In Simcenter STAR-CCM+, simulation operations allow you to construct an automated pipeline for optimization studies without any need for Java macros.
  - Adjoint Shape Optimization: Surface Sensitivity for S-Bend
    An adjoint shape optimization study requires a repeated sequence of steps in which the mesh is deformed according to a deformation field derived from the surface sensitivity. Simulation operations include specific steps that allow you to automate this workflow without requiring Java macros.
  - Adjoint Shape Optimization: Surface Mesh Morphing for Y-Junction Manifold
    Simcenter STAR-CCM+ provides the adjoint solution for shape optimization as a method for finding modified designs to improve performance and efficiency.
  - Adjoint Topology Optimization: Flow through a U-Bend
    Simcenter STAR-CCM+ provides adjoint topology optimization as a method for finding optimized designs for complex flow problems. Topology optimization can aid in the design of, for example, components found in aeroplanes or ground transportation vehicles such as air ducts by improving performance and efficiency.
    - Loading the Starting Simulation File
      You start this tutorial by loading a simulation file that includes the geometry of the topology optimization domain with inlet and outlet channels, pre-defined mesh operations that generate a pseudo 2D trimmed mesh that is two cells thick, and the regions including boundary types. For convenience, you are provided with several pre-defined reports, plots and scenes.
    - Selecting the Physics Models
      The Topology Optimization model determines the optimal distribution of the material within the topology optimization domain by evolving a level-set equation. You use this model together with the Topology Physics model and the Adjoint model.
    - Refining the Material Indicator Interface with Adaptive Mesh Refinement
      Adaptive mesh refinement lets you use a relatively coarse mesh for the topology optimization domain. To drive the local refinement near the solid-fluid interface as it develops, you set up a topology physics mesh adaption criterion which refines the solid-fluid interface mesh based on the gradient of the material indicator field.
    - Specifying the Material Indicator Initial and Boundary Conditions
      The simulation starts with the entire topology optimization domain being filled with fluid. This initial condition provides the highest flexibility for the topology optimization solver in determining the optimal flowpath. For the topology optimization domain only, you specify material indicator values at the boundaries.
    - Defining the Optimization Objective and the Constraint
      The objective of the topology optimization is to minimize the pressure drop between the inlet boundary (high pressure) and the outlet boundary (low pressure).
    - Controlling the Optimization Loops with Stopping Criteria
      One topology optimization loop consists of a sequence of one flow analysis followed by one adjoint analysis. The number of iterations of each flow solution and adjoint solution per loop, and the overall number of loops are controlled by individual stopping criteria.
    - Plotting the Objective and the Constraints
      You plot the evolution of the pressure drop and the solid volume ratio constraint against the optimization iterations. You compare the computed pressure drop against the baseline pressure drop.
    - Visualizing the Topology
      You visualize the topology by observing the distribution of the material indicator. A material indicator value of 1 indicates the position of the fluid in the topology optimization domain.
    - Running the Topology Optimization Using Simulation Operations
      You define a sequence of simulation operations to run the flow analysis and the adjoint analysis alternately. The simulation operations access the previously defined stopping criteria.
    - Analyzing Topology Optimization Results
      You can follow the progress of the solution using the material indicator scene that you created previously.
    - Extracting the Optimized Channel Surface
      You extract the flow channel by creating an isosurface based on the inverted material indicator.
    - Smoothing the Optimized Channel Surface
      The optimized surface that you extracted as a geometry part exhibits a staircase shape in some areas due to the discrete cell resolution of the material indicator field. You use the surface repair tool to smooth the surface in the X and Y directions.
    - Meshing the Smoothed Channel Surface
      Now you replace the topology optimization domain geometry with the optimized channel geometry. Since you perform a flow analysis with the newly composed geometry, you mesh the topology optimized channel with the same number of prism layers and the same cell size as the inlet and outlet channels.
    - Running the Channel Flow Simulation
      You simulate the flow through the optimized channel for 500 iterations in the same Simcenter STAR-CCM+ session, but without adjoint, topology optimization, and adaptive mesh activated.
    - Analyzing the Channel Flow Results
      At the end of the simulation, you judge the quality of the optimization by looking at the velocity vector field and by extracting the pressure drop value of the converged solution.
  - Pareto Optimization: Static Mixer
    One of the study types that Design Manager supports is the multiple-objective tradeoff optimization, also called Pareto optimization. In this type of study, the optimal design is not unique, but a set of designs, which are expressed through a Pareto front. Design Manager searches for the combination of inputs that gives the best overall result among competing objectives.
  - Design Sweep of a Static Mixer
    Design Manager provides an automated method for evaluating the performance of a single product design.
  - Pareto Optimization: 2D Airfoil Design
    Design Manager allows you to run design optimization studies with multiple competing objectives. This type of study is known as multiple-objective tradeoff optimization, or Pareto optimization.
  - Export of 2D Field Data for ROM: Airfoil Analysis
    In Simcenter STAR-CCM+, you can export 2D field data from a design study in Design Manager to Simcenter Reduced Order Modeling.
  - Part-Replacement Using Design Manager
    In some industrial situations you are required to run standard flow analyses on different product configurations for regulatory requirements. Through its part-replacement capability, Design Manager can assist with automating these analyses.
  - Surrogates: Reliability of an Industrial Exhaust System
    Robustness and reliability studies are employed to assess the influence of installation and manufacturing tolerances on the performance of a design. In Design Manager, surrogate models provide an efficient method for running these studies as using surrogates reduces the overall number of flow simulations that must be run.
  - Surrogate FMU: Flapper Valve Representation within Simcenter Amesim
    When a 1D fluid system simulation contains a complex hydraulic component, an FMU built from a surrogate model can represent the influence of the component without requiring a full 1D/3D co-simulation. Design Manager provides a facility for exporting such FMUs based on a surrogate model.
  - Surrogate FMU: Pipeline Junction with Simcenter Flomaster
    Oil transportation from shore-based storage facilities to ships anchored offshore is often analysed using 1D simulation tools such as Simcenter Flomaster. In this tutorial, the flow behavior through a 3D component is characterized using Simcenter STAR-CCM+, exported as an FMU, and introduced within a 1D model of a ship to shore system in Simcenter Flomaster.
  - Smart Sweep Study for Air Compressor: Performance Map
    The operating conditions of an air compressor are stable for mass flow values within a specific range. The operating range varies with the rotational rate of the air compressor rotor. Mass flow values below this range are too low to maintain the desired pressure ratio and can lead to a harmful surge in the air compressor. Mass flow above this range can lead to a choke in the air compressor.
  - Design Manager: Gradient-Based Optimization of a U-Bend
    Gradient-Based Optimization is an iterative method that optimizes a set of geometric features towards an engineering objective.
- Coupling with CAE Codes
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for coupling with CAE codes:
- Analysis Methods
  The tutorials in this set illustrate various Simcenter STAR-CCM+ features for analysing and visualizing simulation data.
- Simcenter STAR-CCM+ In-cylinder
  The tutorials in this set illustrate features for simulating internal combustion engines in Simcenter STAR-CCM+ using the dedicated add-on Simcenter STAR-CCM+ In-cylinder.

Analyzing Topology Optimization Results

You can follow the progress of the solution using the material indicator scene that you created previously.

The following screenshot shows the material indicator distribution and the plots after 300 optimization iterations:

In the material indicator scene, the computed optimized path of the flow is represented by a material indicator value of 1. This topology is embedded into the green solid material denoted by a material indicator value of 0. The topology connects the inlet and the outlet channel around the U-bend geometry that widens at the lower curve. Qualitatively, this flow channel looks acceptable.

In the objective plot, you see that at first the pressure drop varies significantly over the optimization iterations while the algorithm is iterating to meet the objective and the constraint. Then the pressure drop approaches and settles on a constant value around 0.63 Pa. The design objective is met in that this pressure drop value is lower than the baseline pressure drop (given by the blue line).

The constraint plots shows the evolution of the volume ratio constraint against optimization iteration. The volume ratio increases steadily, that is, the fraction of solid material in the domain grows, but to no more than a value of 0. 65 at the end of the simulation.