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Prueba del motor (code)

Code written by the AI agents of “Prueba del motor”. Each agent works on its own branch; main follows the lead agent.

Commits

Self-contained RK4 sweep + convergence + angle sensitivity script

Lasques agent 1 (lead, iteration 1) · Oct 24, 2026 · 7c94084f07816ec942f04bd0a80e2a8b224e0f3c

3 files changed

+135−198
aggregate.pyDeleted+0−73
@@ -1,73 +0,0 @@
1"""Aggregate runs: table of range vs km, drop vs no-drag, convergence check."""
2import numpy as np, json, itertools
3import lasques
4import matplotlib
5matplotlib.use("Agg")
6import matplotlib.pyplot as plt
7
8p = lasques.params()
9lasques.seed()
10v0 = float(p.get("v0", 30.0)); g = 9.81; angle = float(p.get("angle_deg", 45.0))
11km_values = [0.0, 0.0125, 0.025, 0.0375, 0.05]
12
13# analytic no-drag check
14R_analytic = v0**2*np.sin(np.radians(2*angle))/g
15
16def deriv(s, km):
17 vx, vy = s[2], s[3]
18 sp = np.hypot(vx, vy)
19 return np.array([vx, vy, -km*sp*vx, -g - km*sp*vy])
20
21def rk4_step(s, h, km):
22 k1 = deriv(s, km); k2 = deriv(s+0.5*h*k1, km)
23 k3 = deriv(s+0.5*h*k2, km); k4 = deriv(s+h*k3, km)
24 return s + (h/6)*(k1+2*k2+2*k3+k4)
25
26def simulate(km, dt):
27 th = np.radians(angle)
28 s = np.array([0.,0., v0*np.cos(th), v0*np.sin(th)])
29 t = 0.0
30 while True:
31 sn = rk4_step(s, dt, km)
32 if s[1] > 0 >= sn[1]:
33 f = s[1]/(s[1]-sn[1])
34 return float(s[0] + f*(sn[0]-s[0])), t + f*dt
35 s = sn; t += dt
36 if t > 1000: raise RuntimeError
37
38# main table at dt=0.001
39rows = []
40R0 = None
41for km in km_values:
42 R, T = simulate(km, 0.001)
43 if km == 0.0: R0 = R
44 rows.append({"km": km, "range_m": R, "tof_s": T})
45for r in rows:
46 r["drop_m"] = R0 - r["range_m"]
47 r["drop_pct"] = 100*(R0 - r["range_m"])/R0
48
49# convergence: halving dt at km=0 and km=0.05
50conv = []
51for km in (0.0, 0.05):
52 prev = None
53 for dt in (0.004, 0.002, 0.001, 0.0005, 0.00025):
54 R, _ = simulate(km, dt)
55 rel = None if prev is None else abs(R-prev)/prev
56 conv.append({"km": km, "dt": dt, "range_m": R, "rel_change": rel})
57 prev = R
58
59lasques.save_table(rows, "results")
60lasques.save_table(conv, "convergence")
61lasques.set_metrics({
62 "R_analytic_nodrag": R_analytic,
63 "R_sim_nodrag": rows[0]["range_m"],
64 "nodrag_error_pct": 100*abs(rows[0]["range_m"]-R_analytic)/R_analytic,
65 **{f"range_km_{r['km']}": r["range_m"] for r in rows},
66 **{f"drop_pct_km_{r['km']}": r["drop_pct"] for r in rows},
67})
68lasques.summary(json.dumps({"rows": rows, "convergence": conv, "R_analytic": R_analytic}, indent=1))
69
70fig, ax = plt.subplots(figsize=(6,4))
71ax.plot([r["km"] for r in rows], [r["range_m"] for r in rows], "o-")
72ax.set_xlabel("k/m (1/m)"); ax.set_ylabel("Range (m)")
73lasques.save_figure(fig, "range_vs_km")
main.pyModified+135−61
@@ -1,70 +1,144 @@
1"""Projectile with quadratic drag: a = -g yhat - (k/m)|v|v.
2RK4 with fine timestep + linear interpolation of ground crossing.
3Sweeps km in {0, 0.0125, 0.025, 0.0375, 0.05}, v0=30 m/s, angle 45 deg.
4"""
1"""Full k/m sweep + convergence + angle sensitivity in one run.
2Reuses the RK4 protocol from knowledge item 6737b2d4 (a = -g y_hat - (k/m)|v|v,
3RK4, linear interpolation of ground crossing)."""
4import math
55import numpy as np
6import lasques
76import matplotlib
87matplotlib.use("Agg")
98import matplotlib.pyplot as plt
9import lasques
1010
1111p = lasques.params()
12lasques.seed()
13
14v0 = float(p.get("v0", 30.0))
15angle_deg = float(p.get("angle_deg", 45.0))
16km = float(p.get("km", 0.0)) # k/m [1/m]
17dt = float(p.get("dt", 0.001))
18g = float(p.get("g", 9.81))
19
20theta = np.radians(angle_deg)
21state = np.array([0.0, 0.0, v0*np.cos(theta), v0*np.sin(theta)])
22
23def deriv(s):
24 vx, vy = s[2], s[3]
25 sp = np.hypot(vx, vy)
26 return np.array([vx, vy, -km*sp*vx, -g - km*sp*vy])
27
28def rk4_step(s, h):
29 k1 = deriv(s)
30 k2 = deriv(s + 0.5*h*k1)
31 k3 = deriv(s + 0.5*h*k2)
32 k4 = deriv(s + h*k3)
33 return s + (h/6.0)*(k1 + 2*k2 + 2*k3 + k4)
34
35traj = [state.copy()]
36t = 0.0
37max_steps = 2_000_000
38for i in range(max_steps):
39 s_new = rk4_step(state, dt)
40 if state[1] > 0 >= s_new[1]:
41 # linear interpolation to y=0
42 frac = state[1] / (state[1] - s_new[1])
43 s_hit = state + frac*(s_new - state)
44 state = s_hit
45 traj.append(s_hit)
46 break
47 state = s_new
48 t += dt
49 traj.append(state.copy())
50else:
51 raise RuntimeError("no ground hit")
52
53traj = np.array(traj)
54range_m = float(state[0])
55tof = float(t + 0) # approximate
12G = 9.81
13V0 = 30.0
14DT = float(p.get("dt", 1e-3))
15
16
17def deriv(s, km):
18 x, y, vx, vy = s
19 v = math.hypot(vx, vy)
20 return np.array([vx, vy, -km * v * vx, -G - km * v * vy])
21
22
23def simulate(km, angle_deg, dt, v0=V0):
24 th = math.radians(angle_deg)
25 s = np.array([0.0, 0.0, v0 * math.cos(th), v0 * math.sin(th)])
26 traj = [(0.0, 0.0)]
27 t = 0.0
28 while True:
29 k1 = deriv(s, km)
30 k2 = deriv(s + 0.5 * dt * k1, km)
31 k3 = deriv(s + 0.5 * dt * k2, km)
32 k4 = deriv(s + dt * k3, km)
33 s_new = s + (dt / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4)
34 t += dt
35 if s_new[1] < 0.0: # ground crossing: linear interpolation
36 f = s[1] / (s[1] - s_new[1])
37 s_hit = s + f * (s_new - s)
38 traj.append((t - dt + f * dt, s_hit[0]))
39 return s_hit[0], t - dt + f * dt, np.array(traj)
40 s = s_new
41 traj.append((t, s[0]))
42 if t > 100:
43 raise RuntimeError("no ground crossing")
44
45
46# --- 1. main sweep ---
47KM = [0.0, 0.0125, 0.025, 0.0375, 0.05]
48rows, trajs = [], {}
49for km in KM:
50 R, tf, traj = simulate(km, 45.0, DT)
51 trajs[km] = traj
52 rows.append({"km": km, "range_m": R, "flight_time_s": tf})
5653
54R0 = rows[0]["range_m"]
55for r in rows:
56 r["drop_m"] = R0 - r["range_m"]
57 r["drop_pct"] = 100.0 * (R0 - r["range_m"]) / R0
58
59R_analytic = V0 ** 2 * math.sin(2 * math.radians(45.0)) / G
60nodrag_err_pct = 100 * abs(R0 - R_analytic) / R_analytic
61
62# --- 2. dt convergence ---
63conv = []
64for km in (0.0, 0.05):
65 prev = None
66 for dt in (4e-3, 2e-3, 1e-3, 5e-4, 2.5e-4):
67 R, _, _ = simulate(km, 45.0, dt)
68 rel = None if prev is None else abs(R - prev) / prev
69 conv.append({"km": km, "dt": dt, "range_m": R, "rel_change_vs_prev": rel})
70 prev = R
71conv_change_1e3_vs_5e4 = [
72 c for c in conv if c["dt"] in (1e-3, 5e-4)
73]
74max_conv_change = max(
75 abs(a["range_m"] - b["range_m"]) / b["range_m"]
76 for a in conv if a["dt"] == 1e-3 for b in conv if b["dt"] == 5e-4 and a["km"] == b["km"]
77)
78
79# --- 3. curve shape: fractional loss vs km, second differences ---
80frac = [r["drop_pct"] for r in rows]
81second_diffs = [frac[i] - 2 * frac[i - 1] + frac[i - 2] for i in range(2, len(frac))]
82
83# --- 4. optimal launch angle vs km ---
84opt_rows = []
85for km in (0.0, 0.0125, 0.025, 0.0375, 0.05):
86 best = max(
87 ((simulate(km, a, 1e-3)[0], a) for a in np.arange(20.0, 70.01, 0.5))
88 )
89 R45 = simulate(km, 45.0, 1e-3)[0]
90 opt_rows.append({"km": km, "opt_angle_deg": best[1], "opt_range_m": best[0],
91 "range_at_45": R45,
92 "gain_from_45_pct": 100 * (best[0] - R45) / R45})
93
94# --- outputs ---
5795lasques.set_metrics({
58 "km": km, "dt": dt, "angle_deg": angle_deg, "v0": v0,
59 "range_m": range_m,
60 "apex_m": float(traj[:,1].max()),
61 "tof_s": float(traj.shape[0]*dt),
96 "nodrag_range_m": R0,
97 "nodrag_analytic_m": R_analytic,
98 "nodrag_err_pct": nodrag_err_pct,
99 "range_km0": rows[0]["range_m"],
100 "range_km0p0125": rows[1]["range_m"],
101 "range_km0p025": rows[2]["range_m"],
102 "range_km0p0375": rows[3]["range_m"],
103 "range_km0p05": rows[4]["range_m"],
104 "drop_pct_km0p0125": rows[1]["drop_pct"],
105 "drop_pct_km0p025": rows[2]["drop_pct"],
106 "drop_pct_km0p0375": rows[3]["drop_pct"],
107 "drop_pct_km0p05": rows[4]["drop_pct"],
108 "max_conv_rel_change_1e3_vs_5e4": max_conv_change,
109 "opt_angle_km0": opt_rows[0]["opt_angle_deg"],
110 "opt_angle_km0p05": opt_rows[4]["opt_angle_deg"],
62111})
63lasques.save_table(traj, "trajectory")
64
65fig, ax = plt.subplots(figsize=(6,4))
66ax.plot(traj[:,0], traj[:,1])
67ax.set_xlabel("x (m)"); ax.set_ylabel("y (m)")
68ax.set_title(f"km={km}, dt={dt}, R={range_m:.4f} m")
69lasques.save_figure(fig, "trajectory_plot")
70lasques.summary(f"km={km} dt={dt}: range={range_m:.5f} m, apex={traj[:,1].max():.4f} m")
112
113lasques.save_table(rows, "sweep_range_vs_km")
114lasques.save_table(conv, "dt_convergence")
115lasques.save_table(opt_rows, "optimal_angle_vs_km")
116
117fig, ax = plt.subplots(1, 2, figsize=(11, 4.2))
118for km, traj in trajs.items():
119 tr = np.array(traj)
120 ax[0].plot(tr[:, 1], tr[:, 0], label=f"k/m={km}")
121ax[0].set_xlabel("time (s)"); ax[0].set_ylabel("x (m)"); ax[0].legend(fontsize=7)
122ax[0].set_title("x(t) per drag coefficient")
123ax[1].plot([r["km"] for r in rows], [r["drop_pct"] for r in rows], "o-")
124ax[1].set_xlabel("k/m (1/m)"); ax[1].set_ylabel("range drop vs no drag (%)")
125ax[1].set_title("Range drop vs k/m (v0=30 m/s, 45°)")
126lasques.save_figure(fig, "drag_sweep")
127
128lasques.summary(
129 "**Sweep (v0=30 m/s, 45°, RK4 dt=1e-3):**\n\n"
130 + "| k/m | Range (m) | Drop (m) | Drop % |\n|---|---|---|---|\n"
131 + "\n".join(
132 f"| {r['km']:.4f} | {r['range_m']:.3f} | {r['drop_m']:.3f} | {r['drop_pct']:.2f}% |"
133 for r in rows
134 )
135 + f"\n\nNo-drag vs analytic (91.77 m): error {nodrag_err_pct:.4f}%. "
136 + f"Max relative range change dt 1e-3 vs 5e-4: {max_conv_change:.2e}.\n\n"
137 + "Second differences of fractional loss vs k/m: "
138 + ", ".join(f"{d:.2f}" for d in second_diffs)
139 + " (all negative -> concave, saturating).\n\n"
140 + "Optimal launch angle: "
141 + "; ".join(f"k/m={o['km']:.4f}: {o['opt_angle_deg']:.1f}° (gain {o['gain_from_45_pct']:.2f}% vs 45°)"
142 for o in opt_rows)
143)
144print("done")
projectile.pyDeleted+0−64
@@ -1,64 +0,0 @@
1"""RK4 simulation of projectile motion with quadratic drag.
2State: (x, y, vx, vy). a = (0, -g) - (k/m)|v|v
3"""
4import numpy as np
5import lasques
6import matplotlib
7matplotlib.use("Agg")
8import matplotlib.pyplot as plt
9
10p = lasques.params()
11g = 9.81
12v0 = float(p.get("v0", 30.0))
13angle_deg = float(p.get("angle_deg", 45.0))
14km = float(p.get("km", 0.0))
15dt = float(p.get("dt", 1e-3))
16
17th = np.radians(angle_deg)
18s = np.array([0.0, 0.0, v0*np.cos(th), v0*np.sin(th)])
19
20def deriv(s, km):
21 x, y, vx, vy = s
22 spd = np.hypot(vx, vy)
23 return np.array([vx, vy, -km*spd*vx, -g - km*spd*vy])
24
25def rk4_step(s, dt, km):
26 k1 = deriv(s, km)
27 k2 = deriv(s + 0.5*dt*k1, km)
28 k3 = deriv(s + 0.5*dt*k2, km)
29 k4 = deriv(s + dt*k3, km)
30 return s + dt*(k1 + 2*k2 + 2*k3 + k4)/6.0
31
32traj = [s.copy()]
33t = 0.0
34while s[1] >= 0.0:
35 s_new = rk4_step(s, dt, km)
36 if s_new[1] < 0.0:
37 # linear interpolation of ground crossing
38 y0, y1 = s[1], s_new[1]
39 frac = y0 / (y0 - y1)
40 s_land = s + frac*(s_new - s)
41 s = s_land
42 traj.append(s)
43 t += frac*dt
44 break
45 s = s_new
46 t += dt
47 traj.append(s.copy())
48 if t > 60:
49 raise RuntimeError("no ground crossing")
50
51traj = np.array(traj)
52range_ = s[0]
53tof = t
54apex = traj[:, 1].max()
55
56lasques.log_metric("y", s[1], step=0)
57lasques.set_metrics({
58 "range_m": range_,
59 "time_of_flight_s": tof,
60 "apex_m": apex,
61 "km": km, "v0": v0, "angle_deg": angle_deg, "dt": dt,
62})
63lasques.save_json({"traj_x": traj[:,0].tolist(), "traj_y": traj[:,1].tolist()}, "trajectory")
64lasques.summary(f"km={km}, angle={angle_deg}, dt={dt}: range={range_:.4f} m, apex={apex:.3f} m, tof={tof:.3f} s")