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HydroVision Progress Slides

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Meeting 1

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  • Ideas for Thermal Shield
    • Thermal Barrier around Flame
    • Thermal Barrier around Camera
    • Fans Between Flame and Camera
    • Fins Between Camera and Flame
  • Pros and Cons

Kade Nygaard

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  • Thermal Shield Design
    • Reflective Coating (Ag or Al)
    • Material must be resistant to any chemical interactions
    • Cover mainly the front of the camera
  • Thermal Circuit
    • K?
    • Assumptions made
  • Increase distance between camera and flames
    • Connect rectangular shaped material to outer ring where the camera will sit on edge of that same material

Sallury Hernandez

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  • Heat Shield foil like cover over camera lens
  • Possible material aluminum?
  • Ring gear system attached to bottom of rings
  • Gear teeth attached to outer edges of both rings with a fixed inner ring
  • Mount rings with brackets on edges of rings, L-brackets?
  • Thermal circuit, unsure about thermal conductivity and heat rate

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  • Heat Transfer�- do we consider radiation or convection from the surface of the thermal shield to the camera?��- Specific temperatures to include (max flame, shield, and camera)�
  • Rotational Mount�-use a framelike structure to hold down the rotating platform?��-Consider heat transfer of the rotating platform mount?�

Nicholas Nicolai

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Meeting 2

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Designing Heat Shield

  • Thermal Circuit Analysis
    • Matlab to solve the iteration for inner-surface of shield
    • About 350 K or 77 ℃
  • Types of Materials
    • Thermal Conductivity (k↑)
    • Cost
    • Weight requirements
  • Concerns
    • Blocking UV @ 308 nm

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Sallury

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  • Need to center image, account for flat field distortions.

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Matlab Image Processing - Radon Transform

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CAD Modeling

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Meeting 3

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  • Number of pixels/real height*pixel height=Focal length/distance
  • Resolution: 2840x2840
  • Pixel size: 2.74 um x 2.74 um
  • 2840/40*0.00274=0.19454=Focal length/x1
  • 40/40*0.00274=0.00274=Focal length/x2

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  • Angled projections shot at each image�
  • Projections are in parallel from 0-179o for each image�
  • Radon Transform measures the intensity of the projection, displayed using the color bar�
  • Matlab radon function�- sums radon transform of each pixel�- divides each pixel into 4 subpixels�- projects each pixel separately�- split into “bins”�-based on distance between the projection and center of the bin�

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Nicholas Nicolai

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  • Max Torque: 17 Nm
  • 270 deg rotation
  • Max temp @ 70C
  • Other options, varying torque, angles of rotation
  • Cheap option

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  • Require controller and driver
  • Lower Torque
  • Other DC motors, speed controller

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Meeting 4

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Pros:

  • High accuracy and precision
  • Easy to control
  • High torque - low speed
  • Robotics, position holding

Cons:

  • Low efficiency
  • Low speed range
  • Needs controller and driver

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Pros:

  • Efficient
  • Constant torque
  • Brushless durable
  • Speed control with voltage

Cons:

  • Not as easy to control
  • Not as applicable for project

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Pros:

  • Continuous rotating option
  • Higher peak torque at higher speeds
  • Variety
  • Speed control with voltage

Cons:

  • Arduino coding

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  • Camera size calculations:
  • x1=50/0.19454=257.02 mm
  • x2=50/0.00274=18248.175 mm
  • y1/40*0.00274=50/710
  • y1=1028.066 pixels
  • A1=1028.066*1028.066=1056919.7 pixels squared
  • Arduino pseudocode:
  • Read number of pictures, convert into degrees, direct motor to move said degrees, stop for enough time to take the picture, continue moving, repeat until reaches the end, move back to original position.

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Matlab Image Processing

  • Problems�-Code wants to convert original image and not reconstructed image�-Missing major function that isn’t explained in examples�-Expert says there isn’t a fully written code that converts 2D images into tomographic map�
  • Upcoming Tasks�-research test methods that combine slices into a 3D image (couple different methods)�-develop missing function�-debug code

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Meeting 5

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Image Processing and 3D Mapping

  • Determined how to take slices of reconstructed image
  • Any image is able to be used (without color)
  • Trying to figure out how to combine these�
  • Problems:�- 3D reconstruction takes hours of processing time �- when testing, code runs for hours but never actually forms a map�
  • Plans for next week�-try other methods of combining slices into a 3D map�-try to reduce time required to process images