WHAT TO EXPECT
As a Major Casting TMA Lead Analyst, you will focus on the component / system delivery of the durability attribute with the PowerUnit CAE Durability team. You will be responsible for delivering analysis results and solutions to solve durability issues throughout the program from concept through to production. The role demands an extremely strong technical background and understanding of engineering and mathematical models and principals at the highest level. It requires an ability to use these very complex data in a delivery environment and deliver results to plan.
Key Accountabilities and Responsibilities
You will support the concept and design specification of Propulsion System Traction Battery commodities using a variety of specific CAE tools and processes and prepare and run CAE models, post processing of results using commercial & proprietary software. You will be delivering the durability attribute of Traction battery assembly & sub-assemblies in line with PCDS requirements and adhere to System Design Specification and Corporate Requirements.
WHAT YOU'LL NEED
As a Major Casting TMA Lead Analyst, you will have excellent understanding of solid mechanics and the dynamic behaviour of structures, as well as good knowledge of durability assessment tehniques and fatigue calculation theory. You will have a proven track record and experience of PowerUnit Durability delivery / method development within an OEM.
Knowledge, Skills and Experience
Educated to a minimum of Degree level and fundamental knowledge of power unit systems and in particular ICE & EDU propulsion system commodities.Experience of DS Abaqus for linear static / dynamic / forced frequency and non-linear solves, ability to generate shell/solid meshes and apply complex boundary conditions, using JLR tools (Hypermesh, Simlab, Abaqus CAE). * Experienced in thermo-mechanical analysis. Temperature prediction, gasket sealing, bore distortion, stress and fatigue analysis (FEMFAT)
Experience and knowledge of optimisation techniques (Topology / Shape) using FE optimisers (Tosca).