STABILITY IN HIGH-POWERED SOUNDING ROCKETS

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STABILITY IN HIGH-POWERED SOUNDING ROCKETS Hannah Thoreson, ASU/NASA Space Grant Mentor: Dr. James Villarreal ROAR - Robot On A Rocket

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STABILITY IN HIGH-POWERED SOUNDING ROCKETS. ROAR - Robot On A Rocket. Hannah Thoreson , ASU/NASA Space Grant Mentor: Dr. James Villarreal. Payload Separation and Deployment. - PowerPoint PPT Presentation

Transcript of STABILITY IN HIGH-POWERED SOUNDING ROCKETS

Page 1: STABILITY IN HIGH-POWERED SOUNDING ROCKETS

STABILITY IN HIGH-POWEREDSOUNDING ROCKETS

Hannah Thoreson, ASU/NASA Space GrantMentor: Dr. James Villarreal

ROAR - Robot On A Rocket

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Payload Separation and Deployment

OBJECTIVES: Ensure the integrity of the payload during separation from the launch vehicle and deployment of the robotics component of the project. Bring payload in for landing, deployment, and recovery at a velocity that guarantees the safety of bystanders.

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Specifications Payload should be able to withstand the force

of separation 17 ft/s landing velocity Proper orientation of robotics payload upon

ground landing

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Optimization of Impulse Mitigation Plans

0 0.5 1 1.5 2 2.5 3 3.5-2

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2

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14x 10

-3 Impulse Response

Time (sec)

Am

plitu

de

• Spring-damper dashpot system• Matlab program to calculate and plot oscillations from

impulse of parachute deployment• User inputs values for the mass of the combined payload

and housing cabinet, the spring constant, and the damping constant

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Design Outcomes, Pt. I Use of a “slider” to slow the speed of

parachute deployment

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Design Outcomes, Pt. II

Five parachutes, sized to bring craft in at safe landing velocity of 17 fps

“No right side” robot to avoid issues with uncertain landing orientation

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Regression Rate Analysis New project begun in late March with graduate

students Will attempt to predict where combustion

instabilities from pressure fluctuations inside the rocket will occur

Without prediction, there will never be resolution

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Experimental Set-Up

Pressure Transducer

Data Acquisition System

Matlab

Fourier Transform

Human Analysis

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The Fourier Transformfs = 960   % Sample frequency

[data fs] = csvread('data.csv'); % Reads in data from CSV filet = linspace(0,length(data)/fs,length(data)); % Time

plot(t,data)xlabel('Time (seconds)')

ylabel('Pressure Amplitude')title('Time Domain Plot of Pressure')

y = fft(data); % FFT of the dataf_Nyquist = fs/2; % Nyquist frequency

[y_max index] = max(y); % Principle frequencyf = (0:t-1)*(fs/t); % Frequency range

plot(x,y)xlabel('Frequency (Hz)')

ylabel('Pressure')title('FFT Output')

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Expected Outputs

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To be continued!