CFD Simulation Engineer

The Engineer will connect CFD, thermal design, CAD, optimization, manufacturing, and experimental validation to convert high-power-density cooling challenges into scalable products. The position offers growth beyond conventional CFD analysis through ownership of simulation methods, automation, design rules, and customer-facing technical recommendations. 

RESPONSIBILITIES:

• Perform CFD, conjugate heat-transfer, thermal, and Multiphysics analyses for liquid-cooled electronics, with strong emphasis on ANSYS Fluent.

• Develop models for conduction, convection, pressure loss, flow maldistribution, manifolds, interfaces, material behavior, cold plates, microchannels, heat spreaders, and vapor chambers.

• Identify limits in meshing, convergence, model size, solver performance, compute capacity, and simulation time; recommend model reduction, surrogate methods, automation, or additional computing resources.

• Use Python and AI-assisted coding to automate setup, execution, post-processing, optimization, visualization, and reporting. Validate all generated code and models through physics checks, conservation, mesh independence, benchmarks, cross-tool comparison, and test correlation.

• Build simulation DoEs, response surfaces, and Pareto diagrams to balance temperature, thermal resistance, pressure drop, flow uniformity, package limits, manufacturability, robustness, cost, and reliability.

• Convert simulation trade spaces into targeted experimental plans; correlate results, quantify uncertainty, investigate discrepancies, and improve reusable validation methods.

• Translate customer power maps, coolant conditions, allowable temperatures, pressure-drop limits, and package constraints into practical cooling architectures.

Qualifications:

• Bachelor's, Master's, or PhD in Mechanical Engineering, Aerospace Engineering, Physics, Applied Mathematics, Materials Science, or a related discipline.

• 3+ years of industrial simulation or product-development experience.

• Strong ANSYS Fluent capability and practical experience with conjugate heat transfer, internal flow, pressure-drop prediction, meshing, convergence, and model verification.

• Experience with CAD, DoE, optimization, surrogate models, Python automation, and simulation-to-test correlation.

• Ability to distinguish numerical convergence from physical credibility and translate results into manufacturable design recommendations.

• Experience with HPC, AI accelerators, electronics packaging, liquid cooling, or high-power-density systems is highly desirable.