Testing & Diagnosis

Challenge

• Observed a large discrepancy between the simulated and measured drag coefficients
• Needed to understand how air flowed over and around the vehicle
• Required a practical testing method to pinpoint aerodynamic inefficiencies

Approach

• Instrumented the solar car's aeroshell with tufts (light strings)
• Conducted behavioral airflow tests on a closed road highway
• Analyzed tuft motion to identify regions of separation, turbulence, and reversed flow

Solution

• Discovered a "parachute effect" at the leading edge
• Identified a continuous gap between the upper and lower shells
• Used test results to pinpoint areas for refinement in sealing, surface finish

Challenge

• Understand how this particular vehicle responds to controlled road excitations
• Capture accurate motion data from the sprung mass, unsprung masses
• Identify key dynamic behaviors: resonant frequencies, wheel hop, and heave/pitch/roll

Approach

• Instrumented the vehicle and posts with accelerometers in lateral, longitudinal, and vertical axes
• Configured measurement interfaces in Simcenter Testlab
• Performed 1–25 Hz sine sweeps on the shaker posts

Solution

• Identified key resonant peaks at ~2 Hz, ~5.5 Hz, and ~13 Hz
• Verified that CG-level transfer functions exhibited the same resonant trends
• Compared "cobblestone" road input PSD responses to controlled sine inputs

Challenge

• Demonstrate compliance with American Solar Challenge performance rules
• Develop a test plan for a closed-road facility while ensuring safety
• Achieve specified performance targets (≤ 8.0 s/side Figure-8, ≤ 11.5 s Slalom)

Approach

• Developed a formal closed-road test plan specifying facility layout, safety briefings
• Executed course setups per ASC geometry
• Varied tire pressure to establish performance/efficiency tradeoffs

Solution

• Collected, documented, and analyzed telemetry and video for all runs
• Figure-8 and Slalom runs consistently met time targets with clear margins
• Wet braking tests produced average deceleration above ASC minimum

Challenge

• Characterize spark ignition combustion behavior across varying spark timing, engine load, and air-fuel ratio conditions
• Quantify the impact of each parameter on cylinder pressure, heat release rate, indicated efficiency, and burn duration
• Identify and analyze engine knock through pressure data and frequency-domain analysis

Approach

• Collected in-cylinder pressure data across 300 cycles per condition on a spark ignition engine test bench
• Independently varied spark timing (0°, −21°, −31° BTDC), engine load (25%, 62.7%, 100%), and equivalence ratio (φ = 0.78–1.12)
• Computed net/gross IMEP, indicated thermal efficiency, mass fraction burned (CA10/CA50/CA90), heat release rate, and coefficient of variation in MATLAB
• Applied FFT frequency analysis to identify knock signature frequencies in cylinder pressure data

Solution

• Determined that −31° BTDC spark timing yielded the highest indicated efficiency (49.3%) with the shortest burn duration (19.7° CA)
• Found that a slightly lean mixture (φ = 0.9) maximized net indicated efficiency, while rich mixtures reduced efficiency due to incomplete combustion
• Quantified knock's effect on combustion variability—peak pressure variance rose from 1.3 to 30 and heat release rate variance from 50 to 1,760 compared to baseline
• Confirmed knock frequency signature at ~6 kHz via single-sided amplitude spectrum analysis

Challenge

• Use an autonomous line-following car that could not reliably stay on track
• Gain a faster response from the vehicle
• Alter sensitivity and control code to achieve consistent, precise behavior

Approach

• Redesigned the control logic to optimize wheel speed balance
• Implemented logic for lap counting and automatic stopping
• Iteratively tested and tuned system parameters

Solution

• Developed a higher speed and more stable control algorithm
• Achieved consistent track-following performance
• Placed in the top 3 for fastest vehicles around the loop