noisedestroyers
63066ee533
First plate of the hidden-realities series: natural subjects as instrument photographs. One invisible wind field, three detectors — dunes (time-integrated, what the sand remembers), water (instantaneous), milkweed seeds (advected test particles). - src/field/wind.js: divergence-free curl-noise field with closed-form time integral; displacement / scalar / stepped-vec samplers. - src/scene/umbel.js: milkweed head as a particle interaction vertex; burst releases pedicels to seed. src/scene/drift.js: seeds advected, same track contract as track.js. - composition.js: switch -> GROUP_BUILDERS registry; fieldLake/flora/ drift layers + wind plumbing. carpet.js/perspgrid.js: warpFn/phaseFn/ modesFn/heightFn injection hooks (default off, no regression). - schema + composer panels, dunelake ink palette, dune-lake template, tools/dunelake.mjs (17-plate matrix) and its curated SVG set. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
73 lines
3.1 KiB
JavaScript
73 lines
3.1 KiB
JavaScript
/* ============================================================
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field/wind.js — the SHARED WIND FIELD: one invisible field,
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three detectors. The same seeded flow is sampled three ways:
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displacement(x,y,T) time-integrated — what the SAND remembers
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(warps the dune ridgelines)
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vec(x,y,t) / scalar instantaneous — what the WATER shows
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(drives lake ripple phase)
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vec stepped advected — what the SEEDS trace
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(milkweed drift = test particles)
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Divergence-free curl noise from a streamfunction ψ(x,y,t) built
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as a sum of K seeded sinusoidal modes plus a mean drift, so the
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velocity has a closed-form time integral (no numerics, fully
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deterministic). Coordinates are frame-normalized −1..1; typical
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|vec| ≈ strength.
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============================================================ */
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import { makeRng, range } from '../rng.js';
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export function makeWindField(seed, opts = {}) {
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const o = Object.assign({
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modes: 6, scale: 1.6, strength: 1,
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drift: [0.6, 0.05], // prevailing wind (mean velocity, frame units)
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gust: 0.35, // how much the modes stir vs. the mean drift
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}, opts);
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const rng = makeRng(seed, 'wind');
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const modes = [];
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for (let m = 0; m < o.modes; m++) {
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const ang = range(rng, 0, Math.PI * 2);
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const k = range(rng, 0.6, 2.2) * o.scale; // low wavenumber: long swells
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modes.push({
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kx: Math.cos(ang) * k, ky: Math.sin(ang) * k,
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a: range(rng, 0.5, 1) / k, // energy at the large scales
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w: range(rng, 0.4, 1.4), // temporal frequency
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phi: range(rng, 0, Math.PI * 2),
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});
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}
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const norm = modes.reduce((s, m) => s + m.a * Math.hypot(m.kx, m.ky), 0) || 1;
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const g = o.gust * o.strength / norm; // mode velocity normaliser
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const dx0 = o.drift[0] * o.strength, dy0 = o.drift[1] * o.strength;
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// instantaneous velocity: u = ∂ψ/∂y, v = −∂ψ/∂x (divergence-free) + drift
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const vec = (x, y, t = 0) => {
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let u = 0, v = 0;
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for (const m of modes) {
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const c = m.a * Math.cos(m.kx * x + m.ky * y + m.w * t + m.phi);
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u += m.ky * c; v -= m.kx * c;
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}
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return { u: u * g * modes.length + dx0, v: v * g * modes.length + dy0 };
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};
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// Eulerian time integral 0..T of the velocity at a fixed point — the
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// closed-form "memory" of the wind (dune displacement field)
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const displacement = (x, y, T = 1) => {
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let dx = 0, dy = 0;
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for (const m of modes) {
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const p = m.kx * x + m.ky * y + m.phi;
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const s = m.a * (Math.sin(p + m.w * T) - Math.sin(p)) / m.w;
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dx += m.ky * s; dy -= m.kx * s;
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}
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return { dx: dx * g * modes.length + dx0 * T, dy: dy * g * modes.length + dy0 * T };
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};
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// the streamfunction itself — a scalar to phase ripples with
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const scalar = (x, y, t = 0) => {
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let s = 0;
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for (const m of modes) s += m.a * Math.sin(m.kx * x + m.ky * y + m.w * t + m.phi);
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return s * o.strength / (modes.reduce((q, m) => q + m.a, 0) || 1);
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};
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return { vec, displacement, scalar };
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}
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