Our Formula Student car overheated on lap nine, and I was the reason. As thermal-systems lead, I had sized the radiator from a senior team's spreadsheet without questioning its airflow assumptions — assumptions that belonged to the previous car's bodywork. We finished the endurance event with a car limping at half power, and I spent the next month doing what I should have done first: instrumenting the cooling loop, correlating a simple 1-D thermal model against track data, and discovering our real airflow was 40% below the inherited estimate. The redesigned duct and relocated radiator we ran the next season held temperatures with margin. I applied to graduate school because that month was the most alive my education ever felt, and I want two years that feel like it.
My coursework gave me the standard toolkit — thermodynamics, heat transfer, fluid mechanics, FEA — but Formula Student taught me the discipline the toolkit hides: that a simulation is a hypothesis, not an answer, and correlation against measurement is where engineering actually happens. My final-year project extended this instinct, characterizing a small vapor-compression loop for battery cooling and comparing three refrigerant charge levels experimentally against my model's predictions. The model was wrong in instructive ways; my report spent more pages on why than on the results, and my guide called it the most honest thesis he had reviewed that year.
I am applying specifically toward thermal management for electrification — battery packs, power electronics, and the cabin systems that quietly determine an EV's real-world range. The field rewards exactly the model-then-measure discipline I have been practicing at small scale, and it is where mechanical engineering is being asked its hardest current questions.
My goal after the degree is a thermal design role at an EV or battery manufacturer, with the longer ambition of working on thermal systems for the price-sensitive two-wheeler EVs that will electrify India before cars do. A radiator that failed on lap nine started this; I intend to be the engineer whose systems hold margin on lap ninety.
In three years designing HVAC systems for pharmaceutical cleanrooms, I have learned that the most expensive component in the building is an assumption. My employer retrofits sterile manufacturing facilities, where a wrongly specified air-change rate either fails an audit or burns energy for a decade. I made my reputation on a validation project where the client's existing facility kept failing particulate counts in one corridor: rather than adding fan capacity, as proposed, I traced it with smoke studies to a door-interlock timing issue — a fix that cost nothing against a retrofit quoted at forty lakhs. I am applying to graduate school because I keep solving these problems with intuition and handbooks, and I have reached the limit of what intuition and handbooks can defend.
The engineering I want to do next requires computation I currently borrow. Our contamination studies are farmed out to a CFD consultancy whose reports I can question but not reproduce; our energy modeling stops at spreadsheet load calculations when the interesting questions — transient behavior, control strategy, heat recovery sizing — need proper simulation. A master's focused on building energy systems and computational methods converts me from the engineer who reviews these analyses to the one who performs them.
My professional record shows I convert training into practice quickly: I hold the lead-designer role on projects two grades above my seniority band, I introduced psychrometric analysis into a bid process that previously guessed at dehumidification loads, and the door-interlock finding is now a standard checklist item across the firm's audits.
Post-degree, I want to work on energy-efficient cleanroom and cold-chain design — infrastructure India's pharmaceutical and vaccine industries expand every year, mostly with oversized, energy-blind systems. Making sterile air cheap to run is an unglamorous specialization with enormous compounding value, and it is precisely the kind of problem worth a properly trained engineer's career.