Deliverables Other (3) Website Website containing all the publishable information related to the project. This deliverable belongs to Task 7.2. In the deliverable the main website functions will be described Corresponding load cases for the full vehicle models to be used within the project Definition of the load cases for the previous specified vehicles models. This deliverable will be obtained in task 5.1. Report on dissemination to end-user and stakeholders Description of the dissemination activities performed to endusers and stakeholders will be detailed in this deliverable Documents, reports (13) Reduced order models for aerodynamic performance prediction This delievrable will describe the steps taken to define the reduced order models that will be obtained when the task2.2 is completed. Final report containing proposal for further use of the new methods Full report and conclusions to provide the necessary information for the future application of the new method This deliverable is a summary of the work performed in WP5 and belongs to T54 Report on methodological approach for battery risk analysis in severe crash scenarios This delivearble contains the description of the conclusions obtained after the performance of Task 52 related to crash impact on batteries Requirements for setting up a reduced order model of a battery to be used in a full vehicle crash simulation List of the requirements for the ROM of the battery that will be used in the full vehicle crash simulation. This deliverable will result from T5.1. Requirements for setting up a reduced order model of a full vehicle model with parametrized boundary conditions List of the requirements to define the ROM of the full vehicle according to the desired accuracy. This deliverable belongs to T5.1. Requirements for setting up an AI model to improve parallelization of the solver Report of the requirements for the AI model so that the solver can be parallelized his deliverable belongs to Task 53 Requirements for aerothermal simulations reduced order model "This report will list the requirements for the variables necessary to perform the ""offline"" phase to generate the AI/ROM models. This is in terms of which variables, the size of the dataset and the characteristics to obtain the desired accuracy in every case. This deliverable results from Task 2.1. " Potential for ML-based acceleration in finite volume Report of the solver algorithm review and further acceleration potential assessment, focusing on the pressure correction step for the acceleration efforts. This deliverable is a result of ST1.1.1. Assessment of reduced order models for aerodynamic performance prediction Report on the validation process of the previously defined reduced order models used for higher fidelity data. This deliverable results from Task 2.3. Report with the compiled requests for publications Report listing the compiled requests for publications published during the project period This deliverable belongs to Task 73 Validated tool to handle and rationalize aerodynamic data from heterogeneous sources This report will describe the tool implemented for dealing with all types of aerodynamic data coming from different sources and in different formats. This deliverable results from Task 2.4. Assessment of AI/ROM based optimization performances with respect to state-of-the-art methodologies This report will contain the comparison of the new AIROM based technique with the stateofthetechnologies to determine if it results into a better alternative This deliverable results from Task 25 Verification of the optimized model with high fidelity simulations for a fully electric SUV/city car and final report on the framework performance. this deliverable describes a report of verification of results with higher fidelity simulations for the specified vehicles and report describing the full framework This deliverable results from Task 45 Publications Other (2) Application of Physics Informed Machine Learning model for correcting RANS modeled Reynolds Stress Anisotropy Author(s): Bhanu Prakash, Charalampos Tsimis, Enric Aramburu Published in: 2020 Publisher: IDIADA Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering Author(s): Francesco Romor, Marco Tezzele, Markus Mrosek, Carsten Othmer, Gianluigi Rozza Published in: 2022 Publisher: arxiv DOI: 10.48550/arxiv.2110.14396 Conference proceedings (2) Advanced Digitalization for Development of All Types of Electrified Vehicles and Components Author(s): Alain Bouscayrol; Valentin Ivanov; Reinhard Tatschl; Enric Aramburu Published in: 2021 IEEE Vehicle Power and Propulsion Conference (VPPC), 2022 Publisher: IEEE DOI: 10.1109/vppc53923.2021.9699125 AI Enhanced Methods for Virtual Prediction of Short Circuit in Full Vehicle Crash Scenarios Author(s): Alexandre Dumon, Michael Andres, Stefano Menegazzi, Christoph Breitfuss, Cristian Jimenez, Francisco Chinesta, Fatima Daim, Alain Tramecon Published in: SAE Technical Paper Series, 2020 Publisher: SAE International DOI: 10.4271/2020-01-0950 Searching for OpenAIRE data... 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