L8
Multiphysics 3

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15:35
conference time (CEST, Berlin)
Mesh Motion and Smart Adaptive Mesh Refinement Framework for High-fidelity Fluid-structure Interaction Simulations
26/10/2021 15:35 conference time (CEST, Berlin)
Room: L
A. Patel, M. Mehrabadi, S. Pemberton, (Illinois Rocstar LLC, USA)
A. Patel, M. Mehrabadi, S. Pemberton, (Illinois Rocstar LLC, USA)
Today's most challenging problems in numerous technical disciplines involve multiple complex, interacting physical systems, often incorporating different energy exchange mechanisms involving several states of matter. One such domain is fluid-structure interaction (FSI) where fluid and solid structure interaction takes place. Even with the computational advances we have seen in the last couple of decades, large-scale high-fidelity FSI simulations are still cost prohibitive for industrial use. Solving for the phenomena of interest in a given simulation domain requires a certain mesh density. Without the necessary a-priori simulation knowledge, the user is unable to decide the exact areas of interest to refine before the simulation. Solving the problem on a uniformly dense mesh becomes expensive and time-consuming. Adaptive mesh refinement (AMR) technique enables the drastic reduction in computational costs and memory requirements for large-scale multi-physics simulations by refining and coarsening the mesh on the fly where needed during the simulation. Adaptive mesh refinement requires users to manually select the refinement or coarsening thresholds which in turn requires intimate knowledge about the problem and a-priori solution. This limits the usability of adaptive mesh refinement for the broader simulation community. FSI problems involving deforming or oscillating structure inherently requires mesh motion capabilities. Currently, in the open-source software community, a generalized, combined capability of performing adaptive mesh refinement and mesh motion does not exist. To provide a solution to this problem, Illinois Rocstar LLC is developing a generalized mesh motion and machine learning-based smart adaptive mesh refinement framework for FSI simulations with IR's in-house multi-physics simulation suite "Rocstar Multiphysics" and in-house meshing software NEMoSys. NEMoSys smart adaptive mesh refinement and mesh motion capabilities are linked against Rocstar Multiphysics FSI solver to demonstrate the proposed capability. This capability will allow users to perform FSI simulations while leaving adaptive mesh refinement threshold selection and application to the pre-trained machine learning model. The state-of-the-art machine learning network trained on several high-fidelity CFD and FEA problems can correctly identify refinement and coarsening regions in a domain based on the current time-step solution field. This capability is tested on a real-world 3D FSI problem of a wind turbine blade undergoing significant wind loading and deformation/oscillation. Further, we also make assessments on the stability of this combined mesh motion and adaptive mesh refinement capability.
Adaptive Mesh Refinement, Mesh Motion, Fluid-Structure Interaction, Machine Learning, Finite Element Analysis, Computational Fluid Dynamics, Wind Turbine, Rocstar Multiphysics, NEMoSys
15:55
conference time (CEST, Berlin)
Launching Light Weight Multipactor Free Consolidated RF Devices to Space
26/10/2021 15:55 conference time (CEST, Berlin)
Room: L
L. Salman (Ansys Canada Ltd., CAN); S. Acharya, R. Chao (Ansys Inc, USA)
L. Salman (Ansys Canada Ltd., CAN); S. Acharya, R. Chao (Ansys Inc, USA)
High power RF passive components are critical for satellite communications payloads and are required to handle increasingly larger peak/average powers over the entire frequency spectrum in the transmit path of the communication link. For reliability assurance, these high-power RF devices must be proven to be safe and free of high-power related failure phenomena such as multipaction-breakdowns. These industries mainly rely on testing to mitigate system failure. Since it’s very crucial and challenging to reproduce space conditions in lab, the testing procedure is considerably expensive, time-consuming, and unreliable. The presented work leverages the privileges of using Physical-based Simulation technology from Design to Manufacturing Process of an optimized and consolidated passive RF filters that will be safely launched to space for satellite communication. In addition, powder-bed fusion metal additive manufacturing (AM) process has matured as a breakthrough technology for the development of RF and microwave components such as waveguides, filters as well as antennas. This enabling technology has the potential and flexibility to build any shape components that conventional machining can rather be complex or not possible to use. It allows the exploration of unique and optimized designs of parts as well as assembly consolidation. The presented work illustrates the use of multi-physics simulation techniques in the design and optimization of an RF waveguide assembly that integrates three RF functionalities including bending, twisting and filtering sections operating in Ku/K-band [1]. The process simulation methodologies for the selective laser sintering presented in this work allow for reduced waste and build time while maintaining adequate support structure during the manufacturing process. Internal support requirements can generate additional postprocessing challenges. This is where CAE based support generation tools is utilized to find the optimum build time while minimizing the distortion tendencies and the support volume. Once the optimum supports and build orientations are obtained, coupled thermal-structural FEA simulation can be used to simulate the AM build process and estimate the residual stresses for a given build orientation and support strategy. [1] Pevereni O A, Lumia M, Paonessa F, Virone G, Calignano F, Cattano G, Manfredi D, IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 66, NO. 5, MAY 2018
High Power, RF passive components, filters, multipaction breakdown, satellite communication, manufacturing process,Additive Manufacturing (AM), power-bed fusion, 3D Printing, Assembly Consolidation, monolithic waveguide, simulation, residual stress, FEA simulation.
16:15
conference time (CEST, Berlin)
3-D Microstructure Electrochemistry Simulations of Li-Ion Battery Cells with Parameterized Geometries to Assess Plating Risk
26/10/2021 16:15 conference time (CEST, Berlin)
Room: L
S. Srinivasan, P. Rawat, R. Reynolds (Siemens Digital Industries Software, USA); X. Xu, T. Garrick, Y. Zeng (General Motors, USA)
S. Srinivasan, P. Rawat, R. Reynolds (Siemens Digital Industries Software, USA); X. Xu, T. Garrick, Y. Zeng (General Motors, USA)
Lithium plating is detrimental to battery operation from efficiency, ageing and safety perspectives. Lithium deposition at the anode Solid Electrolyte Interface (SEI) is known to occur when the local potential in the anode approaches 0V, and is commonly encountered during Direct Current Fast Charge (DCFC) scenarios at large Charge Rates. Recent studies have suggested a correlation between tortuosity of the porous electrode and plating risk. Since electrolyte tortuosity is determined by Active Material (AM) particle geometry and packing, optimal AM particle statistics are important for cell design from an Electrochemical performance perspective. Further, for a class of electrodes such as the graphite-based anode investigated here, Li intercalation swelling induces stresses due to mechanical constraints imposed by the cell-casing. Consequentially, performance degradation occurs over several charge/discharge cycles due to mechanical fatigue. Crucial to this context, particle shape and stress distribution are linked via current density distribution. Specifically, localization of current density due to local particle geometry and connectivity can result in stress-strain localization due to local Li intercalation behavior. Therefore, an investigation that involves both the electrochemical and mechanical aspects can better guide the microstructural cell design process. Towards this end, we investigate microstructural unit-cell geometry using STAR-CCM+. Key features of our solution approach include: 1. Microstructural Electrochemistry (MSE) simulations providing transient electro-chemical solution including Lithium/Lithium salt concentration throughout the domain. Plating risk can be identified with electric potential distribution. 2. Parameterized anode and cathode particle geometries to study different particle shape, orientation and connectivity statistics. 3. Using Finite Element Analysis (FEA) in STAR-CCM+ to predict mechanical deformation and stress solution based on local particle lithiation from the MSE solution. Simulations are carried out with a few parameterized geometries. The resulting FEA and MSE distributions then help to assess the tradeoffs involved in particle-shape optimization potentially leading towards an effective Multiphysics-based approach to plating mitigation.
Lithium-ion Battery, Microstructural Electrochemistry, Finite Element Analysis, Intercalation, Plating Risk, STAR-CCM+
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