The Joint Advanced Materials and Structures Center of Excellence

Transcription

1 Interpreting Infrared Thermal Images of Repair Sites

2 FAA Sponsored Project Information A. Emery, K. Johnson, UW E. Casterline, J. Lombard, Heatcon Curtis Davies, David Westlund, FAA Technical Monitors Heatcon and Boeing 4/28/2011 2

3 Inverse/Optimal Repair of Composites Goal: To specify the spatial distribution of heat flux from a heating source to produce a specified and constant temperature throughout the cure zone with a minimum of pre-repair testing. Approach: Estimation of convective heat transfer coefficients using inverse numerical methods and Proper Orthogonal Decomposition of thermograms. 4/28/2011 3

4 Finite Element modelling The heat losses, Q, can be characterized by a surface heat transfer coefficient, h, Q=hA T h t h u h s h b 4/28/2011 4

5 Estimation of h s A 27 in. square panel was heated with a thermal blanket under various conditions while temperatures were recorded with thermocouples (TC s) and thermograms (TG s) A finite element model of the experiment was developed using COMSOL Multiphysics which outputs Temperatures. The h values in the finite element model were changed using standard inverse numerical methods until the least squares difference between the measured experimental temperatures and the simulated temperatures was a minimum. 4/28/2011 5

6 Thermocouple Placement and scan lines Surface TC Thermogram line scans Vacuum sniffer 4/28/2011 6

7 Measurements Thermogram scan In Between Temperatures are measured by thermocouples embedded in the composite, placed between the heating Blanket and the composite, and from infrared thermograms embedded 4/28/2011 7

8 Adding a Heat Sink to augment the heat loss Heat loss A channel with air flowing at different rates was used to mimic heat loss to structural members air flow 4/28/2011 8

9 Experimental Setup 4/28/2011 9

10 Program Outline 4/28/

11 Effect of Cooling Air No air flow 6.9 Estimated h b (w/m^2 C) l/m l/m 11.9 Blanket heating density= 2 w/in^2 14.1* 0.8 w/in^ w/in^2 From measured Air flow and air temperatures * Common colors denote the results from the same tests 4/28/

12 Sensitivity of the Inverse Estimation 0 d T/dh TC 9 ht TC 10 ht TC 9 hb TC 10 hb TG 9 ht TG 10 ht TG 9 hb TG 10 hb TC = thermocouple TG = thermogram 9 = over stringer 10 = over thin section time(sec) 4/28/

13 Importance of Panel Properties The finite element model is highly dependent on the geometry and the material properties of the panel; i.e., thermal conductivity, specific heat, density. Literature values for properties of composite materials vary dramatically and are themselves highly dependent on volume fraction, resin type. Without accurate geometric measurements and material property values, estimation of h values is very difficult. 4/28/

14 Proper Orthogonal Decomposition M(t) M M N (x, y) Data N BP (Basic X M (Vectors) Pattern Vectors) Each column of is associated with a specific value of t and will recreate that data vector if is linearly interpolated between t1 and t2, it will produce the data vector that would have been observed at time t if the response were linearly related 4/28/

15 Information Content Each basic pattern vector is orthogonal to all others and the information associated with each vector diminishes as we progress from the 1 st to the last pattern vector 4/28/

16 The Stanford Bunny Let the M data vectors be the N pixel values of each column in the scan and get the basic vectors and see how many are needed to recreate the image? 4/28/

17 Recreation of the Bunny Of the 800 vectors, how many does it take to recreate the Bunny? diffe erences in Pixel Values Stanford Bunny Pixel Values: maximum =238, std dev =65 Maximum Difference Average Difference Std Dev of Differences Basic Vector # 4/28/

18 Energy Content of the Basic Vectors There are a total of 800 vectors, each 756 elements high Energy Content of Basic Vectors Energy Content of Basic Vectors for the Stanford Bunny Energy Energy Basic Vector basic vector 4/28/

19 Typical Thermogram Scans T (C) No Channel Channel, No air flow l/m 100 l/m x 4/28/

20 The Basic Pattern Vectors 180 Y=23, basis vector All look (C) T no sink, no air 130 sink, no air 50 l/min 100 l/min x alike 4/28/

21 But Basic Vectors 3 and 4 show fundamental differences Y=23, basis vector 3 no sink, no air sink, no air 50 l/min 100 l/min Y=23, basis vector 4 T (C) T (C) no sink, no air sink, no air 50 l/min 100 l/min x x 4/28/

22 Conclusions The ability to accurately estimate heat transfer coefficients via standard inverse numerical techniques is highly dependent on the accuracy of the geometry and the material properties used in the finite element model. The basic (i.e., fundamental) patterns of the surface temperature as measured by the thermograms show promise in detecting backside heat flows. We anticipate that we can develop a library of basic patterns that are characteristic of different types of heat loss. What remains is to see if the higher order patterns can be used to quantify the magnitude of the heat losses and thus the needed blanket heating densities. 4/28/

23 Next Step Run tests using different levels of insulation and catalog basic pattern vectors. Insulation Determine if correlation can be drawn between basic patterns and heat losses. If successful this method would avoid all the difficulties involved with the inverse numerical approach i.e., required accurate geometry and material properties, ill conditioned nature of the problem. 4/28/

24 Questions? 4/28/