This project, a collaboration between EnginSoft and the Italian Institute of Nuclear Physics (I.N.F.N.), involved the structural analysis and the design optimization of a Drift Chamber to be mounted on the Mu2e particle detector at FermiLAB in Chicago, Illinois. The ultimate goal of the study was to optimize the Drift Chamber’s performance in terms of stiffness, strength and weight. The nature of this project required in-depth multi-disciplinary knowledge of the best pracitices in design for prototyping as well as expertise in the choice and use of different commercial simulation packages that when used together could produce the optimal results. We were very excited when INFN acknowledged that EnginSoft’s expertise in integrated design chain solutions was a perfect match for these requirements.
The object to be simulated was a drift chamber made of composites to be manufactured by INFN and to be mounted on the Mu2e particle detector at FermiLAB. The particle detector at FermiLAB consists of a linear collider that is able to track and monitor the moving path of a stream of particles before the impact which will occur at the center of the Drift Chamber.
The collider will be employed to detect the presence of sub-atomic particles and for reconstructing their after-impact trajectories. This is done by analyzing the variation of the electric signals produced by hundreds of thousands of pre-loaded electric wires. The analysis of these signals and in particular of their energy levels, together with the study of the after-impact interations between the different particles determine the number as well as the type of such sub-atomic particles produced after the impact. The main structure of the chamber consists of an internal cyclinder enclosed by two external plates.
The Objective Functions for the optimization problem were:
The drift chamber model design involved several analyses that made use of a number of multi-disciplinary commercial software packages whose calculations were coupled using a multiobjective optimization software framework.
First the chamber’s working conditions including the operating temperature, the in-service loads and the boundary conditions were determined.
Next sets of parametric simulations for each particular loading condition were set up in Ansys Workbench:
The composite material configuration covering material sets, reinforcement types and lay-up configuration was generated parametrically using ESAComp, and was coupled with the Ansys tools to feed them with the updated materials definition for each particular design configuration that was evaluated. The parametric definition and its assignment to the geometrical model of the material properties automatically accounted for the manufacturability of each evaluated material system by the coupling in Ansys Composite PrePost (ACP) of the structure with the mesh, geometry sources, ply draping over the mold, ply orientation, fiber-angle correction and ESAComp for the material properties and definitions. Conventional tape laminates and sandwich laminates were analyzed as possible material system solution; the sandwich laminates proved to be the best material choice with respect to the structural objectives that had been set for the project. The material selected consisted of unidirectional (UD) carbon-epoxy prepegs produced by SAATI for the skin reinforcement and carbon foams produced by Graftech for the core portion of the sandwich structure.
ACP was further used to post-process the results of the FEA analyses since local stress components, Saftey Factors (SF) or Reversed Savety Factors (RF) need to be accessed at a single lamina level to evaluate the compliance of each design with the established structural requirements. These requirements expressed in terms of Strength, Stiffness and Weight of the structure were directly calculated within ACP and then used as Objective Functions to be either maximized or minimized during the solution of the overall Design Optimization Problem which was set up in a multi-optimization software framework.
The multioptimization software framework used to couple different software simulation tools and to provide an Automatic Adaptive Design Improvement Strategy by means of Optimization Algorithms, known as Genetic Algorithms (GA), was modeFRONTIER. A value was assigned to each of the deisgn variables/parameters within the multi-optimization software framework to generate a possible candidate design. The required sequence of simulations was run and the results of each simulation were collected in order to rank each design. This was done multiple times for different sets of parameters (designs). The initial population of designs was generated using Design of Experiment Techniques (DOE) and was used to train the optimization GA which was then used to generate designs that produced results that met the prescribed objective functions as close as possible.
EnginSoft delivered a fully parametric design procedure and a ready-to-use collection of tools to solve other challenging problems involving potentially even more complex geometries, material combinations and loading conditions. The final design delivered exceeded expectations in terms of the maximum in-service displacements/deformations allowed, Inverse Reverse Factor (IRF) and weight, whilst at the same time assuring the transparency of the selected material system to the sub-atomic particles. The parametric procedure developed can be reused to quickly determine a new optimal design configuration should the loading/environmental conditions or design requirements be changed or modified to meet new deisgn criteria or specifications.
The structural components of the collider, its finite element discretization, and a contour plot from its buckling analysis
modeFRONTIER provides a seamless coupling with third party engineering tools, enables the automation of the design simulation process, and facilitates analytic decision making.
This case study details the design optimization of an axial steam turbine of 160 MW, focusing on maximizing the total-to-total isentropic efficiency of the last three low-pressure stages of the turbine.
cfd ansys turbomachinery modefrontier