G10
Optimisation 1

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08:35
conference time (CEST, Berlin)
Investigation on the Influence of Different Modeling of Multiple Surface Layers on a 3D Topology Optimization
27/10/2021 08:35 conference time (CEST, Berlin)
Room: G
J. Holoch, R. Renz, S. Lenhardt, A. Albers (Karlsruher Institut für Technologie (IPEK), DEU)
J. Holoch, R. Renz, S. Lenhardt, A. Albers (Karlsruher Institut für Technologie (IPEK), DEU)
Products nowadays are expected to have load-compliant designs as well as a high degree of individuality and design flexibility. In this context, topology optimization in combination with a redesign provides a possibility to generate load-compliant product designs. In terms of achieving a high individuality and design flexibility additive manufacturing processes like selective laser melting (SLM) can be used. SLM is an additive manufacturing process that creates a part layer by layer and each one in two steps. First, the outer contour is formed and afterwards the inner area. This separation ensures that a comparatively high contour accuracy is realized. However, at the same time it results in three areas (contour, interface and hatching) with different material properties due to different cooling rates. To consider these areas including their material properties in a topology optimization, a method is developed to interrupt the topology optimization after each iteration and export the smoothed interim result. Subsequently, the exported interim result is automatically divided into the three areas by offsets using the level set method. In this contribution, a 3D topology optimization is investigated that assigns different isotropic material properties to the corresponding areas after each iteration. After this assignment, the optimization is continued and the described procedure is repeated until the optimization fulfils its convergence criterion. Thus, the influence of such an interruption and change of material properties on the result of the topology optimization is analyzed on a simple part. The results depict, that the methodology tries to maximize the surface area, if Young’s modulus of the contour area is higher in comparison to the hatching area and if Young's modulus in the interface area is lower in comparison to the hatching area. In the future, the method will be extended to include experimentally measured material properties of the SLM process.
additive manufacturing, selective laser melting, SLM, topology optimization, level set, offset generation
08:55
conference time (CEST, Berlin)
Automatic Processing and Cross Section Analysis of Topology Optimization Results
27/10/2021 08:55 conference time (CEST, Berlin)
Room: G
C. Gomes Alves, Y. Barthel (German Aerospace Center / Deutsches Zentrum für Luft- und Raumfahrt e.V., DEU)
C. Gomes Alves, Y. Barthel (German Aerospace Center / Deutsches Zentrum für Luft- und Raumfahrt e.V., DEU)
We propose a comprehensive process chain that considerably reduces the effort and time to successfully interpret topology optimization results and convert them into CAD design proposals. The process chain extracts a wireframe model from a topology optimization result based on a voxelization approach which represents a unique strategy in this field and allows the use of efficient image processing algorithms in the subsequent process steps. The process chain furthermore suggests a suitable cross section for each beam based on the topological geometry as well as stress results calculated during the optimization run. As a first step in the extraction of a wireframe model, the topological finite element mesh-based model is filtered using the relative element density results from the topology optimization, deleting all elements with a relative density below a configurable threshold. The three-dimensional design space of the model is then discretized with a regularly spaced grid – voxels. The filtered FE mesh is converted into a voxel-based representation, thus gaining semi-independence from the arbitrary representation of the FE mesh whose elements can vary in size, form and connectivity. Currently, the FE mesh can consist of any combination of two-dimensional tria and quad elements as well as three-dimensional tetrahedral elements. The now voxel-based representation of the topological model is then thinned using a well-established skeletonization algorithm. From the still voxel-based but one-voxel-thick skeleton lines, structural voxel areas are detected where three or more skeleton lines join together using flood filling algorithms. The centers of these voxel structures are then addressed and connected with lines, creating the wireframe model. With the wireframe model, it is possible to identify beams in the topological model. Each beam in the topology optimization model is cut and the resulting cross section geometrically analyzed to get a first approximate cross section area and shape. This step highly depends on the chosen element density threshold. Furthermore, the topology optimization generates element stresses for each finite element. This data is also analyzed and compared to a defined set of example stress distributions (e.g. tension-compression or bending). Based on the approximate cross section area and the stress analysis results, a suitable cross section for each beam is automatically chosen from a database (e.g. I-section, round, square, …). Future work includes refining the cross section analysis, possibly considering machine learning algorithms, and further processing of the wireframe model, e.g. further FE analyses and a final design proposal in a CAD format.
Automation, Topology Optimization, Code Coupling, Interpreting Optimization Results, Democratisation
09:15
conference time (CEST, Berlin)
Multidisciplinary Analysis and Optimisation of Space Infrastructure: an Industrial Perspective
27/10/2021 09:15 conference time (CEST, Berlin)
Room: G
N. Sarda, J. Moulin, M. Huguenin, J. Olympio, R. Palao (AIRBUS Defence and Space Toulouse, FRA)
N. Sarda, J. Moulin, M. Huguenin, J. Olympio, R. Palao (AIRBUS Defence and Space Toulouse, FRA)
The program DDMS (Digital Design Manufacturing and Services) at Airbus aims to increase the CRL (Capability Readiness Level) of various modelling simulation features, including MDAO (Multi-Disciplinary Analysis and Optimisation) capabilities. Additional objectives of the work described in this paper were to: • show the value-added of the methods and tools on a practical Space System use-case • assess the infrastructure and tools required to deliver the capability on realistically-sized scenarios • develop engineering skills in Airbus’s Space Systems business line • evolve the way of working from: - focusing the design around a single point to answer the customer needs, - to delivering inside the boundary of the design space, a behavioural model able to grasp the 3V challenge: Velocity, Variety, Volume and to propose a set of solutions in adequacy to the customer needs. During the early design stage of spacecraft development, the design freedom is high whereas knowledge about the validity of the design is very low. With traditional spacecraft process development only a small number of iterations can be performed, due to a lack of robust and automated processes covering several disciplines. Thanks to automated workflows using DoE (Design of Experiments) or optimization, hundreds if not thousands of designs can be studied and the design space can be thoroughly evaluated. The objective of this paper is to describe the application of MDAO methods and tools to an industrial use case, namely phase 0 study of a low earth orbit space infrastructure. The complexity of the modelling and implementation, discontinuities in the design space and the wide range of time-scales involved need not to be underestimated when moving from research purpose to industrial deployment. The modelled disciplines are: orbit dynamics, cell illumination, electrical power subsystem (including energy storage and solar array sizing), electric propulsion, power budget, mass budget. Coupling between the various discipline models is performed and, for some disciplines, surrogate based models are created thanks to DoE and model training techniques based on artificial intelligence. The global workflow/dataflow is then executed with multi criteria optimization techniques.
System-level Simulation, Multiscale Simulation, Simulation Governance, Simulation Data Management, Process Simulation, Communicating Complexity
09:35
conference time (CEST, Berlin)
Optimization of Water Cooled Condenser in Combo Washer Dryer Using Computational Fluid Dynamics Simulation
27/10/2021 09:35 conference time (CEST, Berlin)
Room: G
K. Parashar, P. Gandhi (Whirlpool India, IND)
K. Parashar, P. Gandhi (Whirlpool India, IND)
The washing machine has become an integral part of our urban daily life. Usually in western countries there is a separate washer and a dryer in the house. Nowadays customers prefer to have a combined product called as Combo which comprises washer and dryer. This leads to the challenge of managing the drying system in compact space. Year on year the expectation of the customers are raising in terms of the drying time and energy rating for running these machines. So it becomes of utmost importance to have a very efficient machine to dry the clothes with less time as well energy consumption should be low. In that regard, condenser play an important role in combo machines. The main function of condenser is to remove water from the humid air which comes from the clothing drum. So to improve the drying rate without affecting the energy consumption, it is always necessary to have maximum condenser efficiency. In combo washers and dryers generally water cooled condensers are used and since it is a combined unit, there is space constraint for placing the condenser. This project aims to predict early in the design cycle the performance of the water cooled condenser using Computational Fluid Dynamics (CFD) methodology. In this paper, heat transfer analysis is performed using a commercial CFD tool fluent in early stage. Trade off analysis done for different design parameters which can affect the performance of condenser like water flow rate, length, diameter, material of condenser tube, air flow rate and temperature. Critical thinking used through thought map and product map used to identify critical parameters. Virtual DOE used to analyze significant parameters and interaction of those parameters for maximizing the heat transfer rate across the air and condenser tube. Effect of these parameters over the pressure drop in the system is also analyzed to get the trade off between heat transfer and water side/air side pressure drop. In the second step, two phase analysis performed to understand the actual condensation phenomenon using the Eulerian wall film model for the baseline model. The work includes the simulation methodology for predicting the condensate rate by studying the different parameters like turbulence model, mesh method, solver parameter etc. This assessment shows the simulation prediction has a good correlation with the physical test results. Similar study performed for the best design evolved in the first step to understand the improvement in the condenser efficiency. The study encompasses developing a guideline for the simulation by identifying the optimum shape/design of the condenser unit in combo washer and dryer to improve the drying time.
CFD, Condenser, efficiency, Heat transfer, Eulerian wall film, multiphase flow, turbulence, Washing machine.
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