Marcus Tan, M.Eng.
Computational Mechanical Engineer
CFD and Workflow Automation Specialist
Computational Fluid Dynamics · Thermal Management · Workflow Automation · Surrogate Modeling
Marcus Tan, M.Eng. is a computational mechanical engineer specializing in computational fluid dynamics, thermal-fluid systems, and the automation of high-fidelity engineering simulation workflows. His work combines physics-based modeling with Python-driven automation, parametric studies, numerical post-processing, and surrogate modeling to accelerate technically demanding design studies without separating computational speed from physical credibility.
His engineering experience includes thermal management of high-power electronics and semiconductor cooling systems, where he has studied temperature uniformity, pressure loss, coolant distribution, conjugate heat transfer, and system-level thermal architecture. He has developed automated workflows for geometry variation, meshing, solver execution, batch processing, and extraction of engineering performance metrics across large design spaces.
His research background includes reacting flows, heat and mass transfer, electrified reactor concepts, ammonia cracking, hydrogen-production systems, and physics-guided design-space exploration. His work begins with governing balances and clearly defined engineering decisions before progressing to high-fidelity CFD or data-driven surrogate methods.
Areas of Expertise
Computational Fluid Dynamics
High-fidelity CFD for complex fluid-flow, heat-transfer, mass-transfer, reacting-flow, and conjugate thermal systems.
Thermal Management
Cooling of high-power electronics and semiconductor systems, including temperature uniformity, pressure loss, coolant distribution, and thermal architecture.
Simulation Workflow Automation
Python-driven geometry generation, meshing, solver execution, batch processing, parametric studies, and automated result extraction.
Design-Space Exploration
Large parametric ensembles, operating-window evaluation, sensitivity analysis, design comparison, and engineering optimization.
Physics-Guided Surrogate Modeling
High-fidelity data curation, domain-validated surrogate models, reduced-order analysis, and accelerated prediction within verified physical ranges.
Education
- Master of Engineering (M.Eng.) in Mechanical EngineeringKyoto UniversityThesis focus: Computational modeling of multiphase thermal-fluid systems and automation of high-fidelity simulation workflows.2019–2021
- Bachelor of Engineering (B.Eng.) in Mechanical EngineeringKyoto UniversityCapstone work included experimental and numerical investigation of cooling-channel performance.2015–2019
Selected Projects
Automated Design-Space Exploration for Thermal Management Systems
Developed a Python-orchestrated workflow for generating geometry variants, meshing computational domains, launching CFD solvers, and extracting temperature uniformity, pressure-drop, and heat-transfer metrics across large parametric studies.
Physics-Guided Surrogate Modeling for Reactor Design
Supported the development of surrogate models trained on high-fidelity CFD datasets for rapid evaluation of electrified chemical-reactor concepts, with emphasis on remaining within validated operating domains.
System-Level Thermal Optimization
Combined reduced-order energy balances with selective high-fidelity CFD to evaluate component interactions and avoid locally optimized designs that underperform at the system level.
Selected Presentations
- 2025
Accelerating Multiphysics Simulation Pipelines with Scripted Workflows and Domain-Validated Surrogates
AIAA Aviation Forum, Las Vegas, Nevada, USA · July 2025.
M. Tan, “Accelerating Multiphysics Simulation Pipelines with Scripted Workflows and Domain-Validated Surrogates,” AIAA Aviation Forum, Las Vegas, Nevada, USA, July 2025.
- 2024
Physics-Guided Design Space Exploration of Electrified Ammonia Cracking Reactors Using Ensemble CFD and Surrogate Modeling
International Conference on Computational Fluid Dynamics (ICCFD12), Kobe, Japan · July 2024.
M. Tan, “Physics-Guided Design Space Exploration of Electrified Ammonia Cracking Reactors Using Ensemble CFD and Surrogate Modeling,” International Conference on Computational Fluid Dynamics (ICCFD12), Kobe, Japan, July 2024.
- 2023
Automated High-Fidelity CFD Workflows for Parametric Thermal Management of Semiconductor Cooling Systems
ASME International Mechanical Engineering Congress & Exposition (IMECE), New Orleans, Louisiana, USA · October–November 2023.
M. Tan, “Automated High-Fidelity CFD Workflows for Parametric Thermal Management of Semiconductor Cooling Systems,” ASME International Mechanical Engineering Congress & Exposition (IMECE), New Orleans, Louisiana, USA, October–November 2023.
- 2023
System-Level Thermal Optimization: Combining Reduced-Order Energy Balances with Selective High-Fidelity CFD
17th International Heat Transfer Conference (IHTC-17), Cape Town, South Africa · August 2023.
M. Tan, “System-Level Thermal Optimization: Combining Reduced-Order Energy Balances with Selective High-Fidelity CFD,” 17th International Heat Transfer Conference (IHTC-17), Cape Town, South Africa, August 2023.
- 2023
Python-Driven Automation of Coupled Thermal-Fluid Simulations for High-Power Electronics Cooling
ASME-JSME-KSME Joint Fluids Engineering Conference (AJKFED), Osaka, Japan · July 2023.
M. Tan, “Python-Driven Automation of Coupled Thermal-Fluid Simulations for High-Power Electronics Cooling,” ASME-JSME-KSME Joint Fluids Engineering Conference (AJKFED), Osaka, Japan, July 2023.
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