Engineering Physics → Fluid Mechanics
A BSc in Engineering Physics followed by an MSc in Fluid Mechanics established the combination of mathematics, numerical methods, continuum physics and engineering interpretation that still anchors my work.
ENGINEERING BACKGROUND · CAREER DIRECTION
From fluid mechanics to deployable Physics-AI systems
I am an engineer with a foundation in engineering physics and fluid mechanics, more than a decade of CFD and thermal-fluid experience, and a growing focus on turning simulation, reduced-order modeling and AI into usable engineering products and decision tools.
Engineering PhysicsFluid MechanicsCFD / Thermal-FluidsCross-functional EngineeringPhysics-AI
THE THROUGH-LINE
Technical depth first, then broader engineering ownership
My career started from numerical simulation: understanding flow physics, building CFD models, interpreting results and connecting them to engineering decisions. Over time, the scope expanded from simulation itself toward coordination across disciplines, equipment and system integration, safety, suppliers, software, infrastructure and project delivery.
The current direction combines those two sides: retain enough physical and numerical depth to judge a model critically, while building systems that are easier to deploy, communicate and use across engineering teams.
FOUNDATION
Physics, simulation and engineering computation
CFD & thermal-fluid simulation
Roughly a decade of CFD work has covered external aerodynamics, thermodynamics and thermal management, as well as energy and other industrial fluid-flow applications.
Automotive, energy and industrial systems
Projects have included vehicle aerodynamics and thermal management, gas-turbine and thermal-storage topics, and simulation work outside automotive where fluid mechanics supports product or equipment development.
CAREER EVOLUTION
From specialist simulation work to cross-functional engineering
01
CFD ENGINEERING
Model the physics
Build numerical models, assess convergence and fidelity, extract aerodynamic / thermal insight and translate simulation into engineering recommendations.
02
MULTI-SECTOR APPLICATION
Apply the method beyond one product
Work across automotive and energy problems, adapting the same fluid-mechanics foundation to different geometries, operating conditions and design questions.
03
CROSS-FUNCTIONAL ENGINEERING
Own more of the system
Current work extends into equipment planning and project coordination: integrating production, maintenance, safety, fire protection, suppliers, infrastructure, controls and installation constraints around real industrial equipment.
04
PHYSICS-AI DIRECTION
Connect models to deployable capability
The next step is to combine simulation, reduced-order modeling, AI and practical software deployment so high-fidelity engineering knowledge becomes faster to explore and easier for others to use.
WHY THIS PORTFOLIO
The project is deliberately broader than “train an AI model on CFD”
Start from defensible simulation
OpenFOAM/CFD remains the source of truth. Mesh, numerics, convergence, field definitions and validation matter before model compression begins.
Understand the dynamics before choosing the model
Raw POD, phase/residual decomposition, SPOD and SINDy are compared because different flow structures demand different reduced descriptions.
Use learning where it creates leverage
The objective is fast reconstruction, interpolation and eventually broader generalization—not claiming that AI has replaced the CFD solver or the underlying physics.
Make engineering models usable
Cloud workflows, reproducible datasets, APIs, browser visualization and lightweight deployment are part of the technical problem, not an afterthought.
CAREER VISION
Bridge simulation expertise, AI and engineering leadership
I am interested in roles where deep technical understanding and wider ownership meet: leading simulation/AI projects, shaping engineering products, coordinating technical teams, or working directly with customers and stakeholders around complex physics-based technology.
The long-term goal is not to remain confined to one solver, one model family or one industry. It is to build the capability to identify the engineering problem, choose the right fidelity, organize the data and computation, evaluate the model honestly, and turn the result into something that can influence real decisions.
Physics credibilityKnow what the model means and where it can fail.
AI leverageUse data-driven methods to reduce time-to-insight.
Systems thinkingConnect simulation to infrastructure, workflow and deployment.
LeadershipTranslate between specialists, decision-makers and users.
CURRENT PROOF POINT
NACA0012 is the first complete case study
The current project is intentionally modest in geometry but broad in workflow. It demonstrates how I want to work: preserve CFD provenance, compare modeling hypotheses rather than hide failures, develop real-time reconstruction, and carry the result through to an interactive web application.