D15
Multiphysics 7

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08:35
conference time (CEST, Berlin)
Development and Validation of a Fully Coupled Electrical – Thermomechanical Li-ion Cell Model
28/10/2021 08:35 conference time (CEST, Berlin)
Room: D
F. Möller, N. Jakhiya (EDAG Engineering GmbH, DEU)
F. Möller, N. Jakhiya (EDAG Engineering GmbH, DEU)
The use of Li-ion pouch cells in electric vehicles has increased significantly over the past few years. To ensure smooth operation and long life of the battery pack, it is essential to provide optimal conditions for the cells. Appropriate thermal management system has to be designed to maintain the temperature inside the battery pack at optimal condition. A finite element model that can estimate the temperature in a cell is crucial for the design of battery packs and thermal management systems. EDAG has developed a methodology using a fully coupled finite element macro-model that can describe the electrical, thermal and mechanical behaviour of a pouch cell. This methodology is also transferred to cylindrical cells. Internal resistance is an important electrical parameter of a cell. The macro model is built in such a way that its resistance in between two tabs corresponds to the internal resistance of the cell. The internal resistance in the model is also made dependent on State of charge (SOC) and temperature. Subroutines are used to calculate the SOC as a function of current, time and cell capacity. The approach can be extended to ageing dependency if the necessary data is given. The parameters important for the electrical and thermal behaviour are derived from tests. Main parameters are internal resistance with respect to SOC and temperature, cell capacity and specific heat capacity. Other Parameters to describe convection, radiation and conduction are taken from literature study and verified with cooling curves of cells. Multiple charging and discharging tests have been conducted with different current profiles and the temperature measurements from the tests have been used to validate the macro models of pouch cell and cylindrical cell. This method can now be applied to design module layouts with respect to optimal temperature distributions and is the basis -together with a CFD simulation coupling- to design complete cooling systems of batteries in electric vehicles.
Li-ion cell model, cae, fem, fea, validation, thermal behaviour, coupled electrical – thermomechanical model, internal resistance, pouch cell, cylindrical cell
08:55
conference time (CEST, Berlin)
Mastering Aeroelasticity for Transonic Flight with Multiphysics-focused CFD
28/10/2021 08:55 conference time (CEST, Berlin)
Room: D
J. Wirgart (MSC.Software Sweden AB, SWE); S. Hatazawa (Hexagon, JPN)
J. Wirgart (MSC.Software Sweden AB, SWE); S. Hatazawa (Hexagon, JPN)
Aeroelastic flutter analysis is a vital part of civil and military aircraft design and usually performed with a structural code for the subsonic and supersonic airspeeds. However, these codes cannot accurately predict the aeroelastic structural response for transonic airspeeds as unstable shockwaves form over the wings that cause unsteady spatial pressure fluctuations; thus, FSI analysis between CFD and FE codes is required to analyse this problem. Further, the mesh mismatch between CFD and FE codes make it hard to reuse existing simulation models. For example, typical aeroelastic FE models consist of a global shell representation of the airframe that considers stiffness and dynamic behaviour but does not accurately describe the other skin of the aircraft. In contrast, aerodynamic CFD models have a very detailed, fine mesh representation of the aircraft’s skin to accurately capture the lift and drag of the aircraft for different angles of attack. Therefore, FSI analysis often requires the user to create a FE model with a detailed representation of the aircraft’s surface that matches the CFD mesh to map pressure and deformation between the codes, so-called pressure-based load mapping. But even with a dedicated, detailed FE mesh, the pressure-based mapping procedure can introduce considerable errors in the load transfer between the codes. This presentation introduces a force-based load mapping approach between Hexagons scFLOW, a part of Cradle CFD, and MSC Nastran that eliminates the error introduced by the pressure-based mapping. The mapping procedure also supports a single FE shell mesh representing the whole wing structure while still accurately transferring the loads and deformations, including the leading and trailing edges of the wings represented by a free shell edge. Additionally, the presentation covers a quasi-implicit 2-way FSI coupling between scFLOW and MSC Nastran. This coupling allows steady-state CFD simulation to be combined with non-linear static FEA with large rotations to predict the steady condition of a wing profile with or without moving control surfaces for static aeroelastic analysis. Finally, the presentation introduces a dynamic and a static aeroelastic validation case. The dynamic validation case consists of a flutter simulation with comparison against wind tunnel tests of an AGARD 445.6 wing profile under transonic airspeeds. The static case is a study on the lift force produced by the flaperon for different angles of attack of a NACA 65A004 aerofoil, also under transonic airspeeds.
aeroelasticity, transonic, FE, CFD, multi-physics, co-simulation
09:15
conference time (CEST, Berlin)
Advanced Simulation and Uncertainty Quantification of Multiphysics Problems
28/10/2021 09:15 conference time (CEST, Berlin)
Room: D
V. Gravemeier, J.Biehler, J. Nitzler, C. Schmidt, S. Sinzig, W. Wall (AdCo Engineering GW GmbH, DEU)
V. Gravemeier, J.Biehler, J. Nitzler, C. Schmidt, S. Sinzig, W. Wall (AdCo Engineering GW GmbH, DEU)
Taking the ubiquitous multiphysical nature of technical systems or physical, chemical and biological processes, respectively, into account when simulating such problems is inevitable in most of the cases for truly reflecting their real-world features. For this purpose, it is typically both mandatory and challenging to consider all (nonlinear) effects of the individual fields as well as their mutual interactions. Only this way, it is ensured that one obtains reliable simulation results eventually. This is particularly true as soon as one approaches, for instance, the threshold range for dimensioning technical systems. Another important topic for enabling truly predictive multiphysics simulations is that the real-world variabilities and uncertainties, such as unknown physical conditions and parameters, are taken into account by way of an adequate uncertainty quantification approach. Particularly challenging problem configurations in this context arise as soon as their stochastic dimensions are rather high, for instance, when uncertainties vary in space. For such scenarios, standard uncertainty quantification methods are typically not capable of providing a reasonable solution. In this presentation, we will propose an advanced computational method for predictive simulation and uncertainty quantification capable of accurately and efficiently solving challenging large-scale multiphysics problems with high stochastic dimensions. Our approach for quantifying the uncertainties in the case of high stochastic dimensions is based on the so-called Bayesian Multi-Fidelity Monte-Carlo (BMFMC) method. We support the BMFMC method by a physics-informed machine learning approach in that powerful data-oriented machine learning techniques are beneficially combined with actual first principles of physics, such that the application of the proposed computational method is ensured to yield solutions fulfilling the respective physical laws. Results obtained for various multiphysics applications will be presented, such as the simulation and UQ of fluid-structure interaction (FSI; two-field interaction of flow and structure), all-solid-state batteries (ASSB; two- or three-field interaction of electrochemistry, i.e., mass and charge conservation, structure and potentially temperature), and thermal elastohydrodynamic lubrication (TEHL; four-field interaction of lubrication, structure as well as temperature in both lubrication and structure domain).
Multiphysics, Simulation, Uncertainty Quantification, Physics-Informed Machine Learning
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