Your Job:Aim of the thesis is to enhance an existing degradation model for platinum (Pt) in the catalyst layer of a Proton Exchange Membrane Fuel Cell (PEMFC) by implementing advanced numerical methods. The current method to solve the model numerically is based on the Finite Volume Method (FVM) but has limitations in computational speed. This limitation is the bottleneck for fitting routines and analyses that require extensive parameter sweeps. Given the increasing complexity and the need for faster solvers for future model extensions, it is crucial to explore more efficient numerical methods.The focus will be on investigating the potential of the Method of Moments or other suitable approaches to replace or complement the FVM in the existing Python-based framework. The chosen methods should be capable of accurately capturing the key degradation mechanisms, such as Ostwald ripening, coagulation, and particle detachment, while significantly reducing computational time. The ultimate goal is to develop a highly efficient and accurate degradation model that can be easily extended and adapted for future research needs, improving the understanding of Pt degradation in PEMFCs and contributing to the design of more durable and efficient fuel cell systems.Your Tasks:Research and Literature Review: Focus on surveying and understanding numerical methods applicable to Pt degradation modeling in PEMFCs. Include a brief background on PEMFCs, their degradation issues, and relevant modeling approaches.Solver Enhancement: Explore and implement advanced numerical methods to replace the current FVM approach, aiming to improve computational efficiency for fitting routines and future model extensions. Potential methods include the Method of Moments or other suitable approaches.Implementation: Integrate the chosen numerical methods into the existing Python-based degradation model framework. Ensure the enhanced model accurately represents the physical processes involved in Pt degradation in the catalyst layer of PEMFCs.Simulation and Validation: Run simulations to compare the performance of the enhanced solver with the current FVM-based model. Validate the results against experimental data from literature.Analysis and Documentation: Analyze the impact of different numerical methods on the computational efficiency and accuracy of the Pt degradation model. Document the methodology, implementation, and findings.Your Profile:Enrolled in a Master`s program in computational science, physics, mechanical engineering, chemical engineering, materials science or a related field.Proficiency in numerical methods and programming in Python.Knowledge of PEMFC technology and degradation mechanisms is advantageous.Good analytical and problem-solving skills.Ability to work independently and collaboratively within a team.Excellent communication skills in English, both written and spoken.Our Offer:We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:Support from the scientists at IET-3, who have deep and diversified expertise in theoretical electrochemistry and materials modelingOpen-minded and stimulating discussion cultureDiverse opportunities to develop professional skillsClose network with globally leading industrial partnersExcellent prospects and career opportunities in an ever-growing technology sectorAttractive flexitime and homeoffice arrangements and a wide range of options for balancing work and family lifeA reasonable remunerationContract initially for a fixed term of 6 monthsIn addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefitsWe welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.
