Build a reproducible simulation
Bring together a model, parameter sweep, plots and interpretation in a compact scientific computing project.
What you will learn
- Separate parameters from the calculation.
- Record assumptions and units.
- Save outputs and make results reproducible.
The concept
A useful scientific program should be understandable by someone other than its author. Use descriptive variable names, document assumptions, label axes and keep parameter choices separate from the main calculation.
A parameter sweep evaluates a model across a range of input values. It helps reveal trends, sensitivities and possible operating regions, but it does not by itself validate the model against real data.
Worked example
Sweep the slit width and calculate the approximate central maximum width for fixed wavelength and distance.
lambda = 550e-9; L = 1;
slit_width = (20:5:100)*1e-6;
central_width = 2*lambda*L ./ slit_width;
plot(slit_width*1e6, central_width*1e3, 'o-');
xlabel('Slit width (micrometres)');
ylabel('Approx. central width (mm)'); grid on;
% Record model assumptions in your report.The trend is inverse: larger slits produce narrower central maxima. The expression is an approximate small-angle result for ideal far-field diffraction.
Try it yourself
Think about reproducibility before sharing results.
Quick check
Name two things you should record alongside a simulation plot.
Reveal hint and answer
Answer: For example, parameter values and units, model assumptions, software version, initial conditions, or random seed.
Where this is used
This workflow is useful for computational physics, optical design, sensor characterization, research reproducibility and student capstone projects.