Model Tests

Do an interactive visualization of a magnetic field of a line conductor. Include a 3d visualization as well as 2d graphs for Bx, By, Bz. Make it interactive in a way that the user can change the shape of the wire by clicking and dragging. Result should be a static html file.

Do an interactive visualization of a magnetic field of a line conductor. Include a 3d visualization as well as 2d graphs for Bx, By, Bz. Make it interactive in a way that the user can change the shape of the wire by clicking and dragging. Result should be a static html file.

Make a fluid simulation with the following functions: drag fluid by clicking and pressing in the simulation field, sliders for particle density, viscosity, gravity, some more if they make sense. On a phone there should be touch control and the fluid should react to gravity by reading out the phones sensor data via a web api.

Make a fluid simulation with the following functions: drag fluid by clicking and pressing in the simulation field, sliders for particle density, viscosity, gravity, some more if they make sense. On a phone there should be touch control and the fluid should react to gravity by reading out the phones sensor data via a web api.

Build a self-contained HTML file (inline CSS + JavaScript, no external dependencies except optionally a CDN for a charting library) that simulates traffic flow and emergent “phantom jams” on a simple road network.

Road layout: at least one closed-loop circular/oval track, rendered as an SVG or canvas path — a ring is ideal because it lets density stay constant and jams can be observed cycling indefinitely. Optionally a second layout (straight multi-lane highway with wraparound, or a figure-eight) selectable via a dropdown.

Cars as individual agents, each with position (arc-length), velocity, maxSpeed (slight per-car variation, e.g. drawn from a normal distribution), preferredGap, acceleration/deceleration capability, optional reactionTime, and a braking flag for manually triggered slowdowns. Use a car-following model — the Intelligent Driver Model is a good, well-documented choice (gives realistic jam formation from small perturbations); a Nagel–Schreckenberg cellular automaton is fine if you want something lighter.

Controls: car density, desired/max speed, following distance, aggressiveness of braking/acceleration, simulation speed, optionally road length and number of lanes.

Interactive jam trigger: clicking a car forces it into a hard braking event. This should propagate backward through following cars via the car-following model itself — no special-cased “wave” logic, that’s the point. Highlight the clicked car and colour-code cars by speed so the jam wave is visible as it propagates backward against the direction of travel.

Visualization: top-down view of the track, cars coloured by speed; optionally a space-time diagram (x = position, y = time, scrolling), the classic way to see jam waves; live stats for average speed, throughput/flow, and number of jammed cars.

Behaviour: play/pause and reset, live slider updates, smooth requestAnimationFrame, and organized code (separate update/render loop, a Car class, a Road abstraction) so it’s easy to extend.

Goal: demonstrate how, even with no accidents or bottlenecks, a single localized braking event on a sufficiently dense road can spontaneously propagate into a self-sustaining traffic jam — a well-known real-world phenomenon.