HomeEnergy & ThermodynamicsPID Diffusion Front & Arrhenius Diagnostics (2D)

PID Diffusion Front & Arrhenius Diagnostics (2D)

Interactive 2D simulator of Potential-Induced Degradation (PID) in a PV module: a reflected-Brownian-motion ion ensemble that reproduces the classic erfc diffusion-front profile, an Arrhenius ln(D) vs 1/T diagnostic, and a sqrt(time) front-depth linearization chart, all driven by the same drift-diffusion physics as the 3D module simulator.

Energy & Thermodynamics2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version
2d-photovoltaic-degradation-potential-induced ↗ Open standalone

This is the 2D-native counterpart to the 3D Potential-Induced Degradation module simulator. Rather than rendering a cell grid in a 3D scene, it simulates the ion migration mechanism directly: hundreds of sodium ions each perform an independent 1D Brownian random walk from the frame edge, and by the reflection principle their ensemble survival distribution reproduces the exact same erfc drift-diffusion front used by the 3D model — visible here as a live concentration profile converging out of the particle swarm rather than a formula placing particles. A companion Arrhenius panel plots ln(D) against 1000/T with the current operating point marked, and a growing √t linearization strip chart checks the front depth against the straight-line signature that real Fickian diffusion is supposed to produce, turning the same physics used in the 3D degradation model into a genuinely 2D chart-and-particle diagnostic instrument.

⚙ Under the hood

2D-native simulation of Potential-Induced Degradation (PID): hundreds of sodium ions perform independent Brownian random walks from the frame edge, whose ensemble survival distribution reproduces the exact erfc drift-diffusion front from the 3D module model. An Arrhenius ln(D) vs 1000/T diagnostic and a growing sqrt(time) front-depth linearization chart expose the same physics as live, chart-native readouts.

photovoltaicPIDsolar degradationdiffusionbrownian motionarrheniusreliability

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install

What did you find?

Add reproduction steps (optional)