Software Based Model Development for Design of Area Specific Shelter for Thermal Comfort Maintenance.
DRDO · Agriculture, FoodTech & Rural Development · Software
Real physics, free tooling, supplied climate data and almost no competition make this genuinely attractive for a mechanical or civil team, but with no measured data to validate against, the credibility of your submission is entirely in how you justify the boundary conditions.
What it actually is
Shelters in Ladakh warm up nicely during the day because the sun is intense and the sky is clear, and then lose all of it within an hour of sunset because the walls and openings leak heat. Nobody designs those shelters for the specific climate they sit in, so they need fuel-burning heaters to stay habitable. The ask is a simulation model that predicts how warm a shelter will stay given its shape, size, orientation and materials, so you can design one that holds its own heat.
What to build
A parametric thermal simulation model in ANSYS wrapped in a usable interface where a designer enters real ambient climate data and material properties and gets back the three outputs the statement requires — predicted internal temperature over a diurnal cycle, thermal energy captured from solar radiation given the region's irradiance and sunshine hours, and heat flow through envelope and openings for a defined period — with a comparative mode running several material, thermal-mass, shape and orientation combinations under the same ambient conditions and ranking them by how well they hold night-time temperature, so the output is a recommended passive design rather than a single simulation result.
Smallest thing that wins the room
Run the same Ladakh winter diurnal cycle across three envelope options and show one of them holding fifteen degrees more at 2 a.m. than the baseline, with the heat-loss breakdown by wall, roof and glazing explaining exactly why.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 56% of the 226 · #99 of 226 by expected field
A normal-sized field. Your idea has to be good, not miraculous.
Why: defence, intelligence and space bodies drew small fields.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
3/5Genuinely achievable with a very thin field because almost no hackathon team chooses to do finite element thermal work, and the physics is sound and well established, but you have no measured shelter data to validate against so the model's credibility rests entirely on how carefully you set up and justify the boundary conditions.
Feasibility
4/5Everything needed is available — ANSYS student and campus licences are widespread, Ladakh's solar irradiance and sunshine figures are given in the statement itself and published in national renewable energy data, and thermal properties for construction materials and phase change media are standard handbook values.
Innovation scope
2/5The tool is prescribed, the three required outputs are enumerated and passive solar design is a mature discipline with established strategies, so your latitude sits mainly in the parametric wrapper and the comparative ranking rather than in the physics.
Clarity
4/5The three model capabilities are stated explicitly and the statement even supplies the region's irradiance, sunshine duration and clear-day figures, though it never defines the comfort temperature to be maintained, a baseline shelter to improve on, or any measured data to validate the model against.
Effort
HeavyParametric geometry, a correctly set up transient thermal model with solar loading, a material property library, a user-facing wrapper and a comparative study across combinations is substantial simulation work, and getting the boundary conditions and radiation model right takes longer than building the interface.
Demo-ability
MediumTemperature contours and a comparative table are legible and quite persuasive to an engineering panel, but there is no artifact and no interaction — the whole demo is charts, and the result rests on a model nobody in the room can check.
In its favour
- Green flag: The statement supplies Ladakh's irradiance, sunshine duration and clear-day figures directly, so your solar boundary condition is given rather than assumed
- Green flag: Finite element thermal work attracts almost no hackathon entries, so a competent parametric study stands out in a field of dashboards and chatbots
- Green flag: Passive solar strategies — trombe walls, thermal mass, glazing ratio, orientation, night insulation — are a mature literature you can build on and cite rather than inventing your own approach
- Green flag: The comparative mode across material combinations is what turns a simulation into a design tool, and it is also the only part of this statement that produces a recommendation rather than a number
Against it
- Red flag: There is no measured shelter temperature data to validate the model against, so a judge asking how you know the simulation is right has a question you can only answer with methodology rather than evidence
- Red flag: No comfort temperature target and no baseline shelter are specified, so you must define both and a panel may simply disagree with your choices
- Red flag: Simulation studies are unphotogenic and have no artifact, so a team that produces excellent physics and presents it as a wall of contour plots will lose to a worse idea with a better interface
- Red flag: Radiation and infiltration are usually the dominant night-time loss paths in this climate, and a model that handles conduction carefully while treating those crudely will give confidently wrong answers
What you will be writing
- ANSYS transient thermal with solar radiation loading
- parametric geometry sweep via ANSYS Workbench
- phase change material thermal mass modelling
- NASA POWER / NIWE irradiance data ingestion
- envelope U-value and heat loss decomposition
- Python or GUI wrapper for user-defined inputs
- Passive solar building design
- Thermal simulation
- High-altitude infrastructure
Prior art to read before you start
shelter thermal performance simulation · passive thermal mass and material selection · parametric design comparison under climate data
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.