Crank-Slider Mechanism Experiments

Crank Angle (deg),Displacement (mm),Velocity (mm/s),Acceleration (mm/s²),Mechanical Advantage,Rod Angle (deg),Transmission Angle (deg),Crank Torque (N*m)
0,200.00,0.00,-2631.89,0.0000,0.00,90.00,0.0000
30,186.60,-200.12,-1975.43,0.0265,9.59,80.41,-1.0530

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# Download CSV from the simulator or record values from charts
# ============================================================
csv_file = 'data/exp1_baseline.csv'
sim_data = None

if os.path.exists(csv_file):
    with open(csv_file) as f:
        sim_data = list(csv.DictReader(f))
    print(f"Loaded {len(sim_data)} rows from {csv_file}")
else:
    print(f"No CSV found at {csv_file}. Using analytical data only.")
    print("To use simulator data: download CSV from the simulator or")
    print("record values from the charts into a CSV with columns:")
    print("Crank Angle (deg), Displacement (mm), Velocity (mm/s), Acceleration (mm/s²)")

# ============================================================
# STEP 2: Analytical computation
# ============================================================
r, l, e = 50, 150, 0
rpm = 60
omega = rpm * 2 * np.pi / 60

theta = np.arange(0, 361)
theta_rad = np.radians(theta)
sinT = np.sin(theta_rad)
cosT = np.cos(theta_rad)
S = r * sinT - e
Q = np.sqrt(l**2 - S**2)

x_anal = r * cosT + Q
v_anal = -r * omega * (sinT + cosT * S / Q)
a_anal = omega**2 * (-r*cosT + r*sinT*S/Q - r**2*cosT**2*l**2/Q**3)

# ============================================================
# STEP 3: Plot simulator data vs analytical
# ============================================================
fig, axes = plt.subplots(3, 1, figsize=(10, 8), sharex=True)

if sim_data:
    sim_angles = [float(row['Crank Angle (deg)']) for row in sim_data]
    sim_x = [float(row['Displacement (mm)']) for row in sim_data]
    sim_v = [float(row['Velocity (mm/s)']) for row in sim_data]
    sim_a = [float(row['Acceleration (mm/s²)']) for row in sim_data]

    axes[0].plot(sim_angles, sim_x, 'b-', label='Simulator', linewidth=2)
    axes[0].plot(theta, x_anal, 'r--', label='Analytical', linewidth=1)
    axes[1].plot(sim_angles, sim_v, 'g-', label='Simulator', linewidth=2)
    axes[1].plot(theta, v_anal, 'r--', label='Analytical', linewidth=1)
    axes[2].plot(sim_angles, sim_a, 'r-', label='Simulator', linewidth=2)
    axes[2].plot(theta, a_anal, 'b--', label='Analytical', linewidth=1)
    for ax in axes: ax.legend(fontsize=8)

    # Error report
    common = min(len(sim_x), len(x_anal))
    x_err = [abs(s - a) for s, a in zip(sim_x[:common], x_anal[:common])]
    v_err = [abs(s - a) for s, a in zip(sim_v[:common], v_anal[:common])]
    print(f"\nDisplacement: max error = {max(x_err):.4f} mm")
    print(f"Velocity: max error = {max(v_err):.4f} mm/s")
else:
    axes[0].plot(theta, x_anal, 'b-', linewidth=2)
    axes[1].plot(theta, v_anal, 'g-', linewidth=2)
    axes[2].plot(theta, a_anal, 'r-', linewidth=2)

axes[0].set_ylabel('Displacement (mm)')
axes[0].set_title('Experiment 1: Baseline Kinematic Profile (r=50, l=150, e=0)')
axes[1].set_ylabel('Velocity (mm/s)')
axes[1].axhline(y=0, color='k', linewidth=0.5)
axes[2].set_ylabel('Acceleration (mm/s^2)')
axes[2].set_xlabel('Crank Angle (degrees)')
axes[2].axhline(y=0, color='k', linewidth=0.5)

plt.tight_layout()
plt.savefig('plots/experiment_1_results.png', dpi=150)
plt.show()

print(f"\nTDC position (0 deg): {x_anal[0]:.2f} mm")
print(f"BDC position (180 deg): {x_anal[180]:.2f} mm")
print(f"Stroke: {x_anal.max() - x_anal.min():.2f} mm")
print(f"Position at 90 deg: {x_anal[90]:.2f} mm")

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# Run at e=0, e=20, e=-20, download CSV for each
# ============================================================
csv_files = {
    'e=0': 'data/exp2_e0.csv',
    'e=20': 'data/exp2_e20.csv',
    'e=-20': 'data/exp2_e-20.csv',
}
sim_datasets = {}
for label, path in csv_files.items():
    if os.path.exists(path):
        with open(path) as f:
            sim_datasets[label] = list(csv.DictReader(f))
        print(f"Loaded {len(sim_datasets[label])} rows for {label}")
    else:
        print(f"No CSV at {path} (analytical only for {label})")

# ============================================================
# STEP 2: Analytical computation
# ============================================================
r, l, rpm = 50, 150, 60
omega = rpm * 2 * np.pi / 60
theta = np.arange(0, 361)
theta_rad = np.radians(theta)

# ============================================================
# STEP 3: Plot simulator data vs analytical
# ============================================================
fig, axes = plt.subplots(2, 1, figsize=(10, 6), sharex=True)

for e, color, label in [(0, 'blue', 'e=0'), (20, 'red', 'e=20'), (-20, 'green', 'e=-20')]:
    S = r * np.sin(theta_rad) - e
    Q = np.sqrt(l**2 - S**2)
    x_anal = r * np.cos(theta_rad) + Q
    v_anal = -r * omega * (np.sin(theta_rad) + np.cos(theta_rad) * S / Q)

    if label in sim_datasets:
        sim = sim_datasets[label]
        sim_angles = [float(row['Crank Angle (deg)']) for row in sim]
        sim_x = [float(row['Displacement (mm)']) for row in sim]
        sim_v = [float(row['Velocity (mm/s)']) for row in sim]
        axes[0].plot(sim_angles, sim_x, '-', color=color,
                     label=f'{label} (sim)', linewidth=2)
        axes[0].plot(theta, x_anal, '--', color=color,
                     label=f'{label} (anal)', linewidth=1, alpha=0.6)
        axes[1].plot(sim_angles, sim_v, '-', color=color,
                     label=f'{label} (sim)', linewidth=2)
        axes[1].plot(theta, v_anal, '--', color=color,
                     label=f'{label} (anal)', linewidth=1, alpha=0.6)
        # Quick-return: max vs min velocity magnitude
        vmax = max(sim_v)
        vmin = min(sim_v)
        print(f"{label}: max v = {vmax:.2f}, min v = {vmin:.2f}, ratio = {abs(vmin/vmax):.3f}")
    else:
        axes[0].plot(theta, x_anal, color=color, label=label, linewidth=2)
        axes[1].plot(theta, v_anal, color=color, label=label, linewidth=2)

axes[0].set_ylabel('Displacement (mm)')
axes[0].legend(fontsize=8)
axes[0].set_title('Experiment 2: Quick-Return Effect')
axes[1].set_ylabel('Velocity (mm/s)')
axes[1].set_xlabel('Crank Angle (degrees)')
axes[1].axhline(y=0, color='k', linewidth=0.5)
axes[1].legend(fontsize=8)

plt.tight_layout()
plt.savefig('plots/experiment_2_quick_return.png', dpi=150)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# Run at l/r = 1.5, 2, 3, 4 (r=50), download CSV for each
# ============================================================
csv_files = {
    1.5: 'data/exp3_ratio_1.5.csv',
    2: 'data/exp3_ratio_2.csv',
    3: 'data/exp3_ratio_3.csv',
    4: 'data/exp3_ratio_4.csv',
}
sim_datasets = {}
for ratio, path in csv_files.items():
    if os.path.exists(path):
        with open(path) as f:
            sim_datasets[ratio] = list(csv.DictReader(f))
        print(f"Loaded {len(sim_datasets[ratio])} rows for l/r={ratio}")
    else:
        print(f"No CSV at {path} (analytical only for l/r={ratio})")

# ============================================================
# STEP 2: Analytical computation
# ============================================================
r = 50
omega = 60 * 2 * np.pi / 60
theta = np.arange(0, 361)
theta_rad = np.radians(theta)
ratios = [1.5, 2, 3, 4, 5]

# ============================================================
# STEP 3: Plot simulator data vs analytical
# ============================================================
fig, axes = plt.subplots(3, 1, figsize=(10, 9), sharex=True)
colors = ['red', 'orange', 'blue', 'green', 'purple']

for i, ratio in enumerate(ratios):
    l = r * ratio
    sinT = np.sin(theta_rad)
    cosT = np.cos(theta_rad)
    S = r * sinT
    Q = np.sqrt(l**2 - S**2)
    a_anal = omega**2 * (-r*cosT + r*sinT*S/Q - r**2*cosT**2*l**2/Q**3)
    rod_anal = np.degrees(np.arcsin(S / l))
    trans_anal = np.degrees(np.arccos(np.clip(Q / l, -1, 1)))

    label = f'l/r={ratio}'
    if ratio in sim_datasets:
        sim = sim_datasets[ratio]
        sim_angles = [float(row['Crank Angle (deg)']) for row in sim]
        sim_accel = [float(row['Acceleration (mm/s²)']) for row in sim]
        sim_rod = [float(row['Rod Angle (deg)']) for row in sim]
        sim_trans = [float(row['Transmission Angle (deg)']) for row in sim]
        axes[0].plot(sim_angles, sim_accel, '-', color=colors[i],
                     label=f'{label} (sim)', linewidth=2)
        axes[0].plot(theta, a_anal, '--', color=colors[i], linewidth=1, alpha=0.5)
        axes[1].plot(sim_angles, sim_rod, '-', color=colors[i],
                     label=f'{label} (sim)', linewidth=2)
        axes[2].plot(sim_angles, sim_trans, '-', color=colors[i],
                     label=f'{label} (sim)', linewidth=2)
        print(f"l/r={ratio}: peak accel (sim) = {max(abs(a) for a in sim_accel):.1f} mm/s^2")
    else:
        axes[0].plot(theta, a_anal, color=colors[i], label=label, linewidth=1.5)
        axes[1].plot(theta, rod_anal, color=colors[i], label=label, linewidth=1.5)
        axes[2].plot(theta, trans_anal, color=colors[i], label=label, linewidth=1.5)

axes[0].set_ylabel('Acceleration (mm/s^2)')
axes[0].set_title('Experiment 3: Effect of Rod-to-Crank Ratio')
axes[0].legend(fontsize=8)
axes[1].set_ylabel('Rod Angle (degrees)')
axes[1].legend(fontsize=8)
axes[2].set_ylabel('Transmission Angle (degrees)')
axes[2].set_xlabel('Crank Angle (degrees)')
axes[2].axhline(y=40, color='r', linestyle='--', linewidth=0.8, label='40 deg limit')
axes[2].legend(fontsize=8)

plt.tight_layout()
plt.savefig('plots/experiment_3_rod_ratio.png', dpi=150)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# ============================================================
csv_file = 'data/exp5_dead_centers.csv'
sim_data = None
if os.path.exists(csv_file):
    with open(csv_file) as f:
        sim_data = list(csv.DictReader(f))
    print(f"Loaded {len(sim_data)} rows from {csv_file}")
else:
    print(f"No CSV at {csv_file}. Using analytical data only.")

# ============================================================
# STEP 2: Analytical computation
# ============================================================
r, l, e = 50, 150, 0
rpm = 30
omega = rpm * 2 * np.pi / 60
F = 500  # N

theta = np.arange(0, 361)
theta_rad = np.radians(theta)
sinT = np.sin(theta_rad)
cosT = np.cos(theta_rad)
S = r * sinT - e
Q = np.sqrt(l**2 - S**2)

x_anal = r * cosT + Q
v_anal = -r * omega * (sinT + cosT * S / Q)
dxdtheta = r * (sinT + cosT * S / Q)
ma_anal = np.where(np.abs(dxdtheta) > 0.001, 1 / np.abs(dxdtheta), 0)
torque_anal = F * (v_anal / 1000) / omega

# ============================================================
# STEP 3: Plot simulator data vs analytical
# ============================================================
fig, axes = plt.subplots(3, 1, figsize=(10, 9), sharex=True)

if sim_data:
    sim_angles = [float(row['Crank Angle (deg)']) for row in sim_data]
    sim_v = [float(row['Velocity (mm/s)']) for row in sim_data]
    sim_ma = [float(row['Mechanical Advantage']) for row in sim_data]
    sim_torque = [float(row['Crank Torque (N*m)']) for row in sim_data]
    axes[0].plot(sim_angles, sim_v, 'g-', label='Simulator', linewidth=2)
    axes[0].plot(theta, v_anal, 'r--', label='Analytical', linewidth=1)
    axes[1].plot(sim_angles, sim_ma, 'm-', label='Simulator', linewidth=2)
    axes[1].plot(theta, ma_anal, 'r--', label='Analytical', linewidth=1)
    axes[2].plot(sim_angles, sim_torque, 'r-', label='Simulator', linewidth=2)
    axes[2].plot(theta, torque_anal, 'b--', label='Analytical', linewidth=1)
    for ax in axes: ax.legend(fontsize=8)
else:
    axes[0].plot(theta, v_anal, 'g-', linewidth=2)
    axes[1].plot(theta, ma_anal, 'm-', linewidth=2)
    axes[2].plot(theta, torque_anal, 'r-', linewidth=2)

axes[0].set_ylabel('Velocity (mm/s)')
axes[0].axhline(y=0, color='k', linewidth=0.5)
axes[0].axvline(x=0, color='r', linestyle='--', alpha=0.5, label='TDC')
axes[0].axvline(x=180, color='b', linestyle='--', alpha=0.5, label='BDC')
axes[0].set_title('Experiment 5: Dead Centers (r=50, l=150, F=500N, 30 RPM)')
axes[1].set_ylabel('Mechanical Advantage')
axes[1].set_ylim(0, 0.15)
axes[2].set_ylabel('Crank Torque (Nm)')
axes[2].set_xlabel('Crank Angle (degrees)')
axes[2].axhline(y=0, color='k', linewidth=0.5)

plt.tight_layout()
plt.savefig('plots/experiment_5_dead_centers.png', dpi=150)
plt.show()

for angle in [0, 30, 60, 90, 120, 150, 180]:
    print(f"theta={angle:3d}: x={x_anal[angle]:.2f} mm, v={v_anal[angle]:.2f} mm/s, "
          f"MA={ma_anal[angle]:.4f}, T={torque_anal[angle]:.4f} Nm")

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# Run at 30, 60, 120 RPM with F=500N, download CSV for each
# ============================================================
csv_files = {
    '30 RPM': 'data/exp6_30rpm.csv',
    '60 RPM': 'data/exp6_60rpm.csv',
    '120 RPM': 'data/exp6_120rpm.csv',
}
sim_datasets = {}
for label, path in csv_files.items():
    if os.path.exists(path):
        with open(path) as f:
            sim_datasets[label] = list(csv.DictReader(f))
        print(f"Loaded {len(sim_datasets[label])} rows for {label}")
    else:
        print(f"No CSV at {path} (analytical only for {label})")

# ============================================================
# STEP 2: Analytical computation + STEP 3: Plot comparison
# ============================================================
r, l, e = 50, 150, 0
F = 500  # N

fig, ax = plt.subplots(figsize=(10, 5))
rpm_colors = {'30 RPM': 'blue', '60 RPM': 'green', '120 RPM': 'red'}

for rpm, color in [(30, 'blue'), (60, 'green'), (120, 'red')]:
    omega = rpm * 2 * np.pi / 60
    theta = np.arange(0, 361)
    theta_rad = np.radians(theta)
    sinT = np.sin(theta_rad)
    cosT = np.cos(theta_rad)
    S = r * sinT - e
    Q = np.sqrt(l**2 - S**2)
    v_anal = -r * omega * (sinT + cosT * S / Q)
    torque_anal = F * (v_anal / 1000) / omega

    label = f'{rpm} RPM'
    if label in sim_datasets:
        sim = sim_datasets[label]
        sim_angles = [float(row['Crank Angle (deg)']) for row in sim]
        sim_torque = [float(row['Crank Torque (N*m)']) for row in sim]
        ax.plot(sim_angles, sim_torque, '-', color=color,
                label=f'{label} (simulator)', linewidth=2)
        ax.plot(theta, torque_anal, '--', color=color,
                label=f'{label} (analytical)', linewidth=1, alpha=0.6)
        peak_sim = max(abs(t) for t in sim_torque)
        print(f"{label}: peak torque (sim) = {peak_sim:.4f} Nm")
    else:
        ax.plot(theta, torque_anal, color=color, label=label, linewidth=2)

    energy = np.trapz(np.abs(torque_anal), np.radians(theta))
    print(f"{label}: peak torque (anal) = {np.max(np.abs(torque_anal)):.4f} Nm, "
          f"energy/cycle = {energy:.4f} J")

ax.set_xlabel('Crank Angle (degrees)')
ax.set_ylabel('Crank Torque (Nm)')
ax.set_title(f'Experiment 6: Crank Torque vs Speed (F={F}N, r={r}, l={l})')
ax.axhline(y=0, color='k', linewidth=0.5)
ax.legend(fontsize=8)
plt.tight_layout()
plt.savefig('plots/experiment_6_torque.png', dpi=150)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import csv
import os

# ============================================================
# STEP 1: Load CSV data from the simulator
# Run for each parameter variation, download CSV
# Name files: exp8_r30.csv, exp8_l100.csv, exp8_rpm30.csv, etc.
# ============================================================
csv_dir = 'data'
sim_r, sim_l, sim_rpm = {}, {}, {}

for r_val in [30, 40, 50, 60, 70]:
    path = os.path.join(csv_dir, f'exp8_r{r_val}.csv')
    if os.path.exists(path):
        with open(path) as f:
            rows = list(csv.DictReader(f))
        accels = [abs(float(row['Acceleration (mm/s²)'])) for row in rows]
        sim_r[r_val] = max(accels)

for l_val in [100, 125, 150, 175, 200]:
    path = os.path.join(csv_dir, f'exp8_l{l_val}.csv')
    if os.path.exists(path):
        with open(path) as f:
            rows = list(csv.DictReader(f))
        accels = [abs(float(row['Acceleration (mm/s²)'])) for row in rows]
        sim_l[l_val] = max(accels)

for rpm_val in [30, 60, 90, 120]:
    path = os.path.join(csv_dir, f'exp8_rpm{rpm_val}.csv')
    if os.path.exists(path):
        with open(path) as f:
            rows = list(csv.DictReader(f))
        accels = [abs(float(row['Acceleration (mm/s²)'])) for row in rows]
        sim_rpm[rpm_val] = max(accels)

loaded = len(sim_r) + len(sim_l) + len(sim_rpm)
print(f"Loaded simulator data for {loaded} configurations")
if loaded == 0:
    print("No CSV files found. Using analytical data only.")

# ============================================================
# STEP 2: Analytical computation
# ============================================================
theta_rad = np.radians(np.arange(0, 361))

def peak_accel(r, l, e, rpm):
    omega = rpm * 2 * np.pi / 60
    sinT = np.sin(theta_rad)
    cosT = np.cos(theta_rad)
    S = r * sinT - e
    Q = np.sqrt(l**2 - S**2)
    a = omega**2 * (-r*cosT + r*sinT*S/Q - r**2*cosT**2*l**2/Q**3)
    return np.max(np.abs(a))

# ============================================================
# STEP 3: Plot simulator data vs analytical
# ============================================================
r_values = np.arange(20, 75, 5)
a_r = [peak_accel(r, 150, 0, 60) for r in r_values]
l_values = np.arange(80, 220, 10)
a_l = [peak_accel(50, l, 0, 60) for l in l_values]
rpm_values = np.arange(20, 150, 10)
a_rpm = [peak_accel(50, 150, 0, rpm) for rpm in rpm_values]

fig, axes = plt.subplots(1, 3, figsize=(14, 4))

axes[0].plot(r_values, a_r, 'b-', label='Analytical', linewidth=2)
if sim_r:
    sr_x = sorted(sim_r.keys())
    axes[0].plot(sr_x, [sim_r[k] for k in sr_x], 'ro', label='Simulator', markersize=8)
    axes[0].legend(fontsize=8)
axes[0].set_xlabel('Crank Length r (mm)')
axes[0].set_ylabel('Peak |Acceleration| (mm/s^2)')
axes[0].set_title('Sensitivity to r')

axes[1].plot(l_values, a_l, 'r-', label='Analytical', linewidth=2)
if sim_l:
    sl_x = sorted(sim_l.keys())
    axes[1].plot(sl_x, [sim_l[k] for k in sl_x], 'bo', label='Simulator', markersize=8)
    axes[1].legend(fontsize=8)
axes[1].set_xlabel('Rod Length l (mm)')
axes[1].set_title('Sensitivity to l')

axes[2].plot(rpm_values, a_rpm, 'g-', label='Analytical', linewidth=2)
if sim_rpm:
    srpm_x = sorted(sim_rpm.keys())
    axes[2].plot(srpm_x, [sim_rpm[k] for k in srpm_x], 'mo', label='Simulator', markersize=8)
    axes[2].legend(fontsize=8)
axes[2].set_xlabel('Speed (RPM)')
axes[2].set_title('Sensitivity to RPM')

plt.suptitle('Experiment 8: Parametric Sensitivity of Peak Acceleration')
plt.tight_layout()
plt.savefig('plots/experiment_8_sensitivity.png', dpi=150)
plt.show()

print("Key finding: acceleration scales with omega^2 (quadratic with speed)")
print(f"Doubling speed 60->120 RPM: {peak_accel(50,150,0,120)/peak_accel(50,150,0,60):.1f}x increase")