PPolySim OS
Use case · powered by Waste-Heat Recovery

Waste-Heat Recovery for a district heating loop

Simulate a district heating loop live in your browser. This runs the real Waste-Heat Recovery solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

Carnot CycleLive

Controls

Presets

The Carnot cycle — two isothermal and two adiabatic steps — is the most efficient possible heat engine between two temperatures. Its efficiency depends only on the reservoir temperatures: η = 1 − Tc/Th. No real engine can beat it, which is why raising the hot temperature or lowering the cold one is the only path to higher efficiency.

▶ Run in Python

Data Inspector

Efficiency50.0%
Tₕ / Tc2.00
Carnot limit50.0%

Governing equation

Reading this result: Efficiency here is 50.0% and is fixed by the temperature ratio alone — widening the expansion ratio enlarges the enclosed work loop but can never push past the 50.0% Carnot ceiling, and each kelvin removed from the cold side buys a bit more than each kelvin added to the hot side.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Otto Cycle (Engine)Live

Controls

Presets

The Otto cycle idealizes a gasoline engine: adiabatic compression, constant-volume combustion, adiabatic expansion (the power stroke), and exhaust. Its efficiency, η = 1 − 1/r^(γ−1), depends only on the compression ratio. Higher compression means more efficiency — until the fuel pre-ignites and knocks, which is why octane rating matters.

▶ Run in Python

Data Inspector

Efficiency58.5%
Compression ratio9:1
γ1.40

Governing equation

Reading this result: Efficiency (58%) depends only on the 9:1 compression ratio and γ, not on how much fuel you burn — adding heat gives more power at the same efficiency ceiling.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Levelized Cost of EnergyLive

Controls

Presets

LCOE spreads a power project's lifetime cost over every megawatt-hour it produces, letting wildly different technologies be compared fairly. It rewards high capacity factors and cheap capital, and punishes idle plants. The capital-recovery factor discounts future costs to today. It is the number that has made solar and wind the cheapest new power in most of the world.

▶ Run in Python

Data Inspector

LCOE$52/MWh
Per kWh5.2¢
Capacity factor25%

Governing equation

Reading this result: At a 25% capacity factor the plant sits idle most of the time, so its capital is spread over few MWh — that thin denominator is what pushes LCOE up to $52/MWh.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Waste-Heat Recovery tool models a district heating loop with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Waste-Heat Recovery

Other ways to simulate a district heating loop

Frequently asked questions

How do I simulate a district heating loop?
Open this page and use the live Waste-Heat Recovery tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a district heating loop from your own measurements.