Can you provide an example of a time when you had to modify a thermal analysis methodology to improve its accuracy or efficiency?

In my previous role as a Thermal Analyst at XYZ Company, I encountered a situation where I had to modify a thermal analysis methodology to improve its accuracy. We were working on a project for an automotive manufacturer, and the initial methodology we were using was not accurately predicting the temperature distribution within the vehicle's engine compartment. To improve the accuracy, I decided to incorporate additional boundary conditions and refine the mesh density. I also collaborated with the design engineers to gather more accurate data on the internal flow and heat sources. After implementing these modifications, we re-ran the analysis and found that the results were in much closer agreement with the experimental measurements. This modification not only improved the accuracy of the analysis but also helped us identify areas of concern in the design that required further optimization.

In my previous role as a Senior Thermal Analyst at XYZ Company, I encountered a challenging project for an aerospace client where I had to modify a thermal analysis methodology to improve its efficiency. The client's design team had already performed a preliminary thermal analysis using ANSYS Fluent, but the simulation runtime was excessively long, making it impractical for iterative design optimization. Upon review, I identified several areas for improvement. I reconfigured the simulation setup to leverage the parallel processing capabilities of the computing cluster, reducing the runtime by 50%. I also optimized the mesh generation process by dividing the computational domain into smaller subdomains, resulting in improved accuracy and reduced computational resources. Additionally, I implemented a multi-objective optimization algorithm to automatically adjust the maximum and minimum mesh size based on user-defined criteria, further enhancing efficiency. The modifications not only significantly reduced the simulation runtime but also enabled the design team to perform more iterations in less time, ultimately leading to an optimized thermal solution for the aerospace component.

During my tenure as a Senior Thermal Analyst at XYZ Company, I encountered a complex thermal analysis project for a manufacturing client where I had to modify a thermal analysis methodology to improve both accuracy and efficiency. The project involved simulating the cooling of a high-temperature industrial process using COMSOL Multiphysics. The initial methodology employed a simplified approach that did not account for complex flow phenomena and thermal gradients. To boost accuracy, I incorporated multiphase modeling to simulate the interaction between the process fluid, the cooling medium, and the surrounding structures. To mitigate computational costs and improve efficiency, I leveraged domain decomposition techniques to parallelize the simulation across multiple computational nodes, reducing the runtime by 70%. Additionally, I implemented adaptive mesh refinement strategies to concentrate computational resources in regions of interest, resulting in higher accuracy with reduced overall mesh size. The modified methodology provided more accurate predictions of the cooling process, which enabled the client to optimize their operations and minimize energy consumption. It also facilitated the identification of potential hotspots and the design of effective cooling strategies. Overall, my expertise in thermodynamics, heat transfer principles, and proficiency with thermal analysis tools played a crucial role in successfully modifying the methodology for this project.