Diffusion FICK Second Law Simulator All tools
Interactive simulator

Diffusion FICK Second Law Simulator

Evaluate one-dimensional Fickian diffusion through diffusion length, concentration decay, flux index, and reach time.

Parameters
Diffusion coefficient D
m2/s

Input Diffusion coefficient D.

Elapsed time
s

Input Elapsed time.

Evaluation distance
mm

Input Evaluation distance.

Initial concentration
mg/L

Input Initial concentration.

Results
Diffusion length
Concentration ratio
Flux index
Reach time scale
Concentration profile
Diffusion length and time scale
D-time concentration map
Model and equations

$$L_d=\sqrt{2Dt},\quad C(x,t)=C_0\exp\left(-\frac{x^2}{4Dt}\right)$$

This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks.

How to read it

Use the main plot to read the controlling trend, including break points that a single result card can hide.

Use the sensitivity view to find input combinations where margin collapses quickly.

For early design, focus on which input controls margin before trusting the absolute value.

Learn Diffusion FICK Second Law by dialogue

🙋
When reading Diffusion FICK Second Law, where should I look first? Moving Diffusion coefficient D changes both the plots and the result cards.
🎓
Start with Diffusion length, but do not treat the number as the whole answer. Use Concentration profile to confirm the assumed state, then read Diffusion length and time scale for the distribution or trend. Use the main plot to read the controlling trend, including break points that a single result card can hide.
🙋
I can see why Diffusion coefficient D changes Diffusion length. How should I judge the influence of Elapsed time?
🎓
Move Elapsed time in small steps and watch Concentration ratio. That reveals which term is controlling the result. This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. A single operating point is not enough; sweep the realistic scatter range.
🙋
What is D-time concentration map for? It feels like the ordinary curve already tells the story.
🎓
D-time concentration map is for finding boundaries where the condition becomes risky or margin collapses quickly. Use the sensitivity view to find input combinations where margin collapses quickly. In First-pass comparison of design options before review, the important question is often what happens after a small change, not only the nominal value.
🙋
So if Diffusion length is within the target, can I accept the condition?
🎓
Treat this as a first-pass review. It helps with Narrowing controlling factors and worst-side conditions before detailed analysis and Teaching or explaining the equation, numbers, and visualization under the same inputs, but final decisions still need standards, measured data, detailed analysis, and vendor limits. For early design, focus on which input controls margin before trusting the absolute value.

Practical use

First-pass comparison of design options before review.

Narrowing controlling factors and worst-side conditions before detailed analysis.

Teaching or explaining the equation, numbers, and visualization under the same inputs.

FAQ

Start with Diffusion length and Concentration ratio. Then use Concentration profile to confirm the assumed state and Diffusion length and time scale to read distribution or bias. Use the main plot to read the controlling trend, including break points that a single result card can hide
Move Diffusion coefficient D alone, then move Elapsed time by a comparable amount and compare the change in Diffusion length. D-time concentration map shows combinations where margin or performance changes quickly.
Use it for First-pass comparison of design options before review. Instead of trusting a single point, widen the input range and check whether Diffusion length keeps enough margin before moving to detailed analysis.
This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. Final decisions still require standards, measured data, detailed analysis, and vendor limits.