Lab Overview

Air compressor and storage tank
C-D nozzle with 13 pressure taps
Schlieren optical system

Schlieren Imaging & Wind Tunnel Components

Schlieren imaging is an optical technique that visualizes density gradients in a flow field by detecting how light rays refract as they pass through regions of varying refractive index — directly proportional to local density. In a supersonic nozzle, this reveals shocks and expansion fans as sharp bright or dark bands without any physical intrusion into the flow.

The ERAU supersonic tunnel uses a blowdown configuration: compressed air stored in a high-pressure tank is released through a main valve, regulated to a set stagnation pressure, and accelerated through a C-D nozzle. The divergent section expands the flow to supersonic speeds. Pressure taps along the nozzle floor sample the static pressure distribution during the run window, and the Schlieren system captures still images of the test section throughout.

Schlieren image — shock wave (Fig. 2.1)
Schlieren image — shock detail (Fig. 2.2)
Annotated nozzle cross-section

Results & Analysis

Pressure vs Tap Position (Fig. 3.1)
Pressure vs Row Points (Fig. 3.2)
NPR and shock strength summary

MATLAB Code

Raw pressure data was read from a CSV file, averaged over the steady-state window (rows 1,653–2,103), and augmented with atmospheric pressure. Total pressure was back-calculated from the throat Mach number, and the mass flow rate computed from choked-flow theory. Below is a representative excerpt; the full script is AE315_LAB5.m.

% Constants
Patm = 14.7;       % psi
T0   = 27;         % °C ambient
T    = T0*(9/5) + 491.67;   % → Rankine
y    = 1.4;
R    = 1716;       % ft·lbf/(slug·°R)
Ath  = 0.765;      % throat area (in²)

% Steady-state average + add atmospheric pressure
range_ss  = tempData(1653:2103, :);
dataframe = mean(range_ss) + Patm;    % 13-element vector (psi)

% Back-calculate total pressure from throat tap (Mach 1)
pTotal = dataframe(2);
P0     = pTotal / (1 + (y-1)/2)^(-y/(y-1));

% Nozzle pressure ratio
Pb  = mean(dataframe(11));   % back-pressure tap
NPR = P0 / Pb;               % = 3.932

% Shock strength between taps 9 and 10
shock = dataframe(10) / dataframe(9);  % = 1.6142

% Choked-flow mass rate (lbm/s)
a    = sqrt(y * R * T);
mdot = Ath * (P0/a) * sqrt(y) * ((2/(y+1))^((y+1)/(2*(y-1)))) * 32.174; % = 6.1036
Full MATLAB script — AE315_LAB5.m
Pressure_data.csv layout (13 taps)
Steady-state window selection logic

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