thermal imaging and uncertainties in the interpretation...

International Journal of Contemporary ENERGY, Vol. 2, No. 2 (2016) ISSN 2363-6440 ___________________________________________________________________________________________________________

___________________________________________________________________________________________________________ G. Laštovička-Medin: “Thermal Imaging and Uncertainties in the Interpretation: Case Study”, pp. 29–41 29

DOI: 10.14621/ce.20160204

Thermal Imaging and Uncertainties in the Interpretation: Case Study

Gordana Laštovička-Medin

Faculty of Sciences and Mathematics, University of Montenegro

Džodrdža Vašingtona bb, 20000 Podgorica, Montenegro Abstract This paper presents a case study of thermal imaging. It explores the challenges in the interpretation of thermal imaging. In particular, it analyses the uncertainties and the sources of inaccuracies. The rapid technological development leads to the paradoxical situation where there is a higher amount of users of thermal imaging than the amount of those who understand the physics behind it and know how to interpret the colourful images of the false colour displays since it is sometimes very difficult to quantitatively describe observations due to background interfering conditions such as thermal reflection, shadow effects of nearby objects etc. This paper tackles this problem. It focuses on the identification and qualitative interpretation of source uncertainties. It is not intended to be an exhaustive list of all possible uncertainties but rather shows many qualitative examples. Images shown here were taken as part of the Low Carbon Household Thermal Image Survey. The given information might be beneficial for those who want to make their own household energy loss investigations and for those who want to use thermal imaging in as an educational tool for the visualizations of phenomena in physics and chemistry related to energy transfer. In particular this paper can be beneficial for those who want to study the cross-correlation effect amongst independent and uncorrelated uncertainty sources as well as to link the investigations of accuracy in measurement interpretations to the investigations of ambient features and distant dependant measurements.

1. Introduction Thermal imaging technology has become increasingly popular at colleges and universities in both the classrooms and the labs. It can help to visualize and thereby enhance the understanding of physical phenomena from mechanics, thermal physics, electromagnetism, optics and radiation physics, qualitatively as well as quantitatively, in an interactive and engaging way [1], [2], [3]. A few concepts that can be easily visualized with a thermal imaging camera include: the thermal properties of materials and objects, heat conduction, convection, radiation, heat insulation and friction.

This paper explores the applications of thermal imaging for enegy loss visualisations. The work was done by the supportive and informal Low Community Carbon Group [4]. The aim was to find out how to reduce carbon footprint substantially and to raise awareness of climate change locally and at the same time promoting a more sustainable lifestyle through redusing the energy consumption and increasing the energy efficiency. The qualitative analyses were only done without providing a metrological evaluation of the commercially available infrared camera, since only one camera type had been used. The camera type was Flir. For methodology research we refer to [3], where the author suggests how to best estimate the accuracy of thermal imaging instruments, whilst considering the level of accuracy attributed to measurements from these thermal imagers. The paper is structured as follows. It begins with an introduction to the infrared thermography applications. Then the terminology and concepts are explained and followed by a description of the property of measurement and uncertainty sources. The uncertainty issues such as emissivity and reflectivity were explored in Chapter 7, whilst Chapter 8 presents the interpretation of images. The measurement error analyses of a thermal imaging were only considered

Keywords: Thermal imaging; Uncertainties; Interpretation; Emissivity; Reflection; Infrared radiation

Article history: Received: 26 April 2016 Revised: 30 October 2016 Accepted: 03 November 2016



qualitatively since for the quantitative analyses we would need a more accurate knowledge of a materials’ emmisivity to what we didn’t have reliable access. While it is reasonable to interpret uncertainties qualitatively, the quantitative interpretation of the measured temperatures can only be undertaken once they have been converted into meaningful temperatures.

2. Infrared thermograph application Thermal imaging can be helpful in quantifying heat losses and for condition monitoring. It can also be used effectively to identify changes in the condition of an object over time by spotting a trend of changing temperatures. Most frequently, thermal imaging is used to find abnormalities or unusual patterns of radiated heat which are indicative of a fault or excessive heat loss.

The use of thermal imaging technology in industries and in science is illustrated in Figure 1 [5]. Additionally there is a growing field of medical diagnostic thermography (breast abnormalities, thyroid abnormalities, musculoscelatal, peripheral vascular, cerebral vascular, inflammatory and neoplastic conditions). The reason to use thermal imaging technology in medical diagnostics because temperature is a very good indicator of health, as changes of just a few degrees on the skin (cutaneous or superficial) can be used as an indicator of possible illnesses [6]. For example, IRT is used to detect superficial body tumors, such as breast cancer [7]. Tumors generally have an increased blood supply that increases the skin temperature over them [8]. Therefore, IRT can be used as an effective early indicator of breast cancer [9], which results in a much higher chance of survival [10]. In these applications, IRT is a complementary diagnostic tool with high efficiency only in the detection of early warning signals. This early

Figure 1. Different aspect of thermal imaging applicatios [5]



Figure 2. Medical diagnostic thermography [16]

Figure 3. Medical diagnostic thermography [18] detection is the main advantage of IRT compared with other methods. IRT is used in many other medical applications, such as the diagnosing of diabetic neuropathy or vascular disorders [11], fever screening [12], skin diseases [13], dentistry and dermatology [14] and heart operations [15]. Moreover, functional infrared thermal imaging (fITI) is considered as an upcoming, promising methodology in psychophysiology (Figure 2) [16]. Furthemore, functional infrared imaging was also used to study the facial thermal signatures of three fundamental emotional conditions: stress, fear and pleasure arousal [17]. Neurodevelopmental disorder

characterized by impaired social interaction, verbal and non-verbal communication, and restricted and repetitive behaviour such as autism can be monitored using the thermal imaging technology as well. Figure 3 [18] shows images captured during the treatment of autistic children. It clearly idicates behavioural change in body temperature emissions before and after treatment. For more details in medical Infrared Imaging we refer to [19].

To conclude, number of applications for thermal imaging is rapidly growing because IRT has many advantages over other technologies [20]. In general, the



main advantages of IRT are the following: IRT is a non-contact technology: the devices used are not in contact with the source of heat, i.e., they are non-contact thermometers; IRT provides two-dimensional thermal images, which make a comparison between areas of the target possible, IRT is in real time, which enables not only high-speed scanning of stationary targets, but also acquisition from fast-moving targets and from fast-changing thermal patterns; IRT has none of the harmful radiation effects of technologies, such as X-ray imaging and thus, it is suitable for prolonged and repeated use; IRT is a non-invasive technique, thus, it does not intrude upon the target or affect it in any way and it can also be easily incorporated into neurological and psychological studies or work with children with behaviour disorders and neurological diversity [21]. However, IRT is not without its drawbacks. Fast and affordable hardware has recently become available, but an infrared camera is still an expensive device. Other, inexpensive models with high spatial resolution provide lower accuracy, which makes them unusable for some applications. Infrared images can also be difficult to interpret; in general, specific training is required. IRT is also highly dependent on working conditions, such as the surrounding temperature, airflow or humidity. Thus, progress on accuracy of image interpretation in needed.

3. Physics laws of infrared radiation In order to acquire good knowledge of image interpretation it is important to firstly understand the important terms and concepts behind them as well as the procedure of converting thermal (infrared radiation) into electric signal which visualizes the invisible (to

human eye – infrared) energy to a visible image on the camera’s screen.

Infrared measuring devices acquire infrared radiation emitted by an object and transform it into an electronic signal [22]. Thermal radiation is electromagnetic radiation emitted over a range of wavelengths by an object related to the object’s temperature. The infrared ray is part of electromagnetic radiation covering the “band” of wavelength between 0.78 m to 1000 m as shown in Figure 4 [23].

The temperature of the object, along with other factors, determines the intensity at each wavelength. Radiation thermography is the use of a sensor to measure the thermal radiation emitted by an object, generally with the intent to determine the object’s temperature [24]. Sensors often contain only a single sensing element and yield a single measurement value. By contrast, a thermal camera uses a focal plane array (FPA), which is an array of sensors. By displaying the array of measured values, an image is formed which is a representation of the temperature distribution on the surface of the object. Each element of the array is a pixel. Each pixel corresponds to a location in space of the scene being imaged, this is called a scene. True temperature refers to the actual temperature of an object. Apparent temperature refers to the temperature reported by a ‘perfect’ camera, and includes properties of the scene being imaged such as emissivity and reflections [24]. Note that this is not necessarily the same as the imaged temperature, which is the temperature reported by the actual camera, and also includes properties of the image acquisition process such as scattering in the camera optics. It is important to remember that thermal cameras do not actually measure temperature. They

Figure 4. The electromagnetic spectrum [23]



measure intensity of electromagnetic radiation within a range of wavelengths during a period of time (the integration time of the camera). The difference between a visible image and an infrared image is that the visible image is a representation of the reflected light on the scene, whereas in the infrared image, the scene is the source and can be observed by an infrared camera without light. Images acquired using infrared cameras are converted into visible images by assigning a colour to each infrared energy level. The result is a false-colour image called a thermogram [25].

The calibration of the camera allows the user to convert these measured intensities to imaged temperatures. Imaged temperatures may be converted to apparent temperatures, and apparent temperatures may then be converted to true temperatures, if one has a qualitative and quantitative understanding of the physical properties of the objects, the characteristics of the camera, as well as the conditions encountered while acquiring the images. Attaining accurate true temperatures is generally the goal when using thermal cameras. The wavelengths of light being detected depend on the spectral response of the camera and any filters used [24]

In order to understand the difference between apparent temperature and true temperature, it is important firstly to understand four basic laws of infrared (IR) radiation: Kirchhoff's law of thermal radiation, Stefan-Boltzmann law, Planck’s law, and Wien’s displacement law. The characteristics of thermal radiation depend on various properties of the surface it is emanating from, including its temperature, its spectral absorptivity and spectral emissive power, as expressed by Kirchhoff's law of thermal radiation which states that: “For a body of any arbitrary material emitting and absorbing thermal electromagnetic radiation at every wavelength in thermodynamic equilibrium, the ratio of its emissive power to its dimensionless coefficient of absorption is equal to a universal function only of radiative wavelength and temperature. That universal function describes the perfect black-body emissive power”. [26].

It is important to note that the thermal radiation is not monochromatic, i.e., it does not consist of just a single frequency, but comprises a continuous dispersion of photon energies, its characteristic spectrum. If the radiating body and its surface are in thermodynamic equilibrium and the surface has perfect absorptivity at all wavelengths, it is characterized as a black body. A black body is also a perfect emitter. The radiation of such perfect emitters is called black-body radiation. Planck's law describes the spectrum of blackbody radiation, which depends only on the object's temperature. The temperature determines the wavelength distribution of the electromagnetic radiation. Thus, the distribution of power that a black

body emits with varying frequency is described by Planck's law. At any given temperature, there is a frequency fmax at which the power emitted is a maximum. Wien's displacement law, and the fact that the frequency is inversely proportional to the wavelength, indicates that the peak frequency fmax is proportional to the absolute temperature T of the black body. Wien's displacement law determines the most likely frequency of the emitted radiation, and the Stefan–Boltzmann law gives the radiant intensity [27]. The intensity of the radiation depends on the temperature and nature of the materials’ surface. At lower temperatures, the majority of this thermal radiation is at longer wavelengths. As the object becomes hotter, the radiation intensity rapidly increases and the peak of the radiation shifts towards shorter wavelengths. The relationship between the total radiation intensity (all wavelengths) and temperature is defined by the Stefan- Boltzmann law which reflect relationship between the power radiated by a dense hot body and the temperature (P = e A σ T4 (W) where the variable T represents the absolute temperature, A is the surface area of the radiator, and e is the emissivity, a function of emitted wave length; for a perfect black body e = 1 and the Stefan Boltzmann Constant, σ, is equal to 5.67 x 10-8 W/(m2 K4). The Plank and the Stefan- Boltzmann laws are linked as follows. Energy radiated from the blackbody is described by Planck’s Law but in order to obtain total radiant emittance of the blackbody, the equation describing Plank's law has to be integrated over all wavelengths (0 to infinity). The result is the Stefan- Boltzmann equation. In order to find out the wavelength on the maximum spectral radiant emittance, differentiate Planck’s law and take the value to 0.

4. The concept of working the thermal

imaging camera The concept of working the thermal imaging camera (See Figure 5 [23]) is as follows: Infrared energy (A) coming from an object is focused by the optics (B) onto an infrared detector (C). The detector sends the information to the sensor elecronics (D) for image processing. The electronics translate the data coming from the detector into an image (E) that can be viewed on the viewfinder or on a standard video monitor or LCD screen [23].

Infrared thermography is the art of transforming an infrared image into a radiometric one, which allows temperature values to be read from the image. So every pixel in the radiometric image is in fact a temperatue measurement. In order to do this, complex algorithm are incrporated into thermal image camera.



Figure 5. Thermal imaging camera [23]

Figure 6. The comparison between the visualization of what a IR thermometer and a thermal camera see [23]

Thermal imaging cameras detect and measure the sum of infrared energy over a range of wavelengths determined by the sensitivity of the camera’s detector. Thermal imagers cannot discriminate energy at 7µm from energy at 14µm the way the human eye can distinguish various wavelengths of light as colour. They calculate the temperature objects by detecting and quantifying the emitted energy over the operational wavelength range of the detector. The temperature is then calculated by relating the measured energy to the temperature of a blackbody radiating an equivalent amount of energy according to Planck’s Blackbody Law.

5. Infrared thermometer vs. imaging camera It is important to distinguish IR thermometer from the thermal imaging camera. Infrared (IR) thermometer are reliable and very useful for single spot temperature reading, but when scanning large areas, it is easy to miss critical parts like the air leakages [23]. A comparison between the images captured by infrared thermometer and those captured by a thermal imaging camera is displayed in Figure 6 [23]. With an infrared thermometer one is able to measure the temperature at one single spot. FLIR thermal imaging cameras can measure temperature on the entire images. For example FLIR (Forward Looking InfraRed) i3 has an image resolution of 60 x 60 pixels. This means tha it is equal to using 3600 IR thermomeers at the same time.

6. Properties of measurement and nature of

uncertainty sources Identification, characterization and verification of uncertainties when detecting the loss of heat due to faulty insulation are complex procedures. The possible errors assigned to a measurement system itself is the following: calibration of camera sensitivity and systematic offsets in sensor, conversion of apparent to true temperature, camera optics, electronic effects, instrument error from instrument noise, integrated averaging of radiance over increasing pixel area due to increased viewing distance (decreased resolution) etc. Other uncertainty sources are linked to ambient conditions such as weather humidity, atmospheric pressure and ambient temperature. For example infrared sensed data can be subjected to errors greatly due to atmospheric attenuation by atmospheric scattering caused by particulate material in the atmosphere and absorption by gases. The errors arising from viewing the surface at an oblique angle can significantly affect the measurement accuracy and thus the validity of data/image interpretation. The uncertainty in calibration of camera, sensitivity and offset may be linked to incorrect adjustment of camera to emissivity of inspected object. Furthermore, the dependency of camera sensitivity towards polarization of the measured thermal radiation brings uncertainty as well. So to improve accuracy the camera system must vary as a function of polarization angle. Also an incomplete understanding of resolution (the smallest possible distance between two values of measurement) and repeatability (the range of values attained by repeated measurements under the same conditionals) may be a significant source of uncertainty. For instance, since the response of camera is a linear function of intensity but it is not a linear function of temperature, as a consequence resolution of the temperature



measurement depends on the temperature being measured and not on true temperature. In some cases the effect of non-uniform emissivity can be avoid using long integration time. Moreover one of the essential issues is to apply the suitable form of Plank equation in order to convert apparent true temperature. As clearly explained in [28] there are multiple modification of Plank’s equation in the literature. As many parameters (reflectivity, transmittance, atmospheric absorption, changes in emissivity as a function of temperature) are taken into account, the more realistic description of energy loss can be achieved.

However, there are many issues unique to a measurement procedure and each source of uncertainty may be of greater or lesser importance depending on the specific quantity measured. For example, in certain cases camera sensitivity and pixel cross talk are of major concern. By contrast, when measuring a peak temperature motion blue effects are likely to be more important. So it is important to understand how those error sources affect the measurement uncertainty and to link the investigation of errors to the investigation of uncertainties because they do not exclude each other. In some cases the investigation of components of the combined standard uncertainty requires the conducting the study for uncorrelated input variables of the measurement model because estimation of the influence of cross-correlations among the variables on measurement accuracy is an important issue. The influence of correlations depends on measurement conditions and these may differ significantly. For example, the combined standard uncertainty may depend strongly on the correlation between variables representing the object emissivity and the ambient temperature. Additionally, variables representing the ambient and atmospheric temperature may affect the temperature measurement uncertainty to some extent as well. To determine which uncertainty sources are of most concern it is crucial to consider which are important to the question being asked. As with any measurement system there are sources of uncertainty that must be understood to fully assess the quality of the measurement result. It is also useful to remember that any temperature measurement is valid only at some location during some time interval. This leads to characterisation of uncertainties such as amplitude, temporal and special [28]. For instance if temperature a short distance away have very different amplitudes due to large thermal gradients in the image, even a modest spatial uncertainty can cause a large amplitude uncertainty regardless of how well the sensitivity of the camera is known. There are many other issues involved with uncertain analysis which are described elsewhere [29].

7. Uncertainty sources: Emissivity and reflectivity

Interpreting a thermal image takes a degree of skill and understanding. Different materials and surfaces emit heat at different rates, so some allowance must be made for the construction of each different object inspected. Additionally, thermal imaging cameras cannot distinguish between emitted and reflected infrared energy so the user must be aware of any other sources of infrared energy that may cause reflection and must be able to recognise these in thermograms. Angular variation in emissivity may also bring uncertainty in data interpretaton so analysis with increased viewing distances as they vary both along the view path (atmospheric effects) and across the image (viewing distance and atmospheric effects) especially if viewing obliquely would increase image interpretation accuracy. Thus in order to get the correct interpretation of thermal images it is important to put the object of inspection into the environmental context, taking into account the texture and interaction of materials with radiation as well. An assessment of how accurately the attenuation and distance measurements need to be made in order to obtain useful temperature measurements is essential. It is important to look at all the possible interferences between object and surrounding that may affect the way object's features appear in a camera's display. For instance wind, humidity or pressure can mislead the meaning of image captured by camera. Further, since materials have different thermal conductivity, the difference in thermal conductivity of materials can lead to large temperature variations in certain situations. Reflection and emission are other issues which have to be carefully taken into consideration. Those issues will be discussed later. The ambient temperature may also affect the result and alter the accuracy significantly. For instance, high ambient temperature can mask hot spots by heating the entire object while a low ambient temperature might cool down the hot spots to a temperature below a previously determined threshold. However, not only weather conditions, but also indoor and outdoor temperature can alter the accuracy of interpretations and mislead the conclusion. The nature of ventilation and heating system in the household may bring additional uncertainty in measurement interpretation.

Among the issues previously mentioned, the emissivity is one of the key unfamiliar concepts causing the inaccurate interpretation. The reason is explained as follows. Emissivity is defined [30] as the ratio of the radiance emitted by a surface to the radiation emitted by blackbody at the same temperature. The spectral-directional emissivity of a surface at a given temperature is the ratio of the radiance of the radiation emitted at a particular wavelength in a particular direction to the



radiance of the radiation emitted by blackbody at the same temperature. Thus, the emissivity is a dimensionless number between 0 (for a perfect reflector) and 1 (for a perfect emitter). The physical interaction of material and matter is here seen as follows. The emissivity of a surface depends not only on the material but also on the nature of the surface. Additionally, it is important to clarify the emissivity variation with variation of angle viewing. For instance, authors in articles [31, 32] showed experimentally that the emissivity of a surface may change as a function of the viewing angle to that surface, with maximum emissivity at normal viewing angles to the emitting surface[33]. Authors in article [32] measured the apparent temperature of various samples (soils and sand) at a fixed temperature using a spectro-radiometer over a range of viewing angles. The spectro-radiometer measured the IR radiation reflected from a series of mirrors one of which was attached to a goniometer. This mirror could be rotated about all angles (00–900) from the horizontal and reflects the IR radiation from the target sample.

Moreover, the texture of surface and the emissivity variation with angle viewing are correlated to a certain extension. For instance, if a surface is a diffuse then there will be no change in emissivity with angle. Thus, knowledge of the surface is essential both for accurate non-contact temperature measurement and for heat transfer calculations. When viewing ‘real’ more reflective surfaces, with a lower emissivity, less radiation will be received by the thermometer than from a blackbody at the same temperature and so the surface will appear colder than it is unless the thermometer reading is adjusted to take into account the material surface emissivity. To summarize, given two objects with the same true temperature but different emissivity, a higher apparent temperature will be calculated for the object with higher emissivity. Given two objects with the same emissivity but different true temperature, a higher apparent temperature will be calculated for the object with higher true temperature. The apparent temperature of an object may be substantially different from its true temperature. Only when the emissivity of objects is known can thermal imagers compensate for emissivity and calculate true temperature.

Another adjustment which has to be carefully considered is reflectivity. Objects with high reflectivity can reflect energy radiated by other objects. For example, polished aluminium reflects about 90% of the energy incident upon its surface. Just as thermal imagers cannot detect the emissivity of objects in order to calculate their true temperature, they also can’t detect the reflectivity of objects. Therefore, when calculating the apparent temperature of an object, thermal imagers detect and quantify energy emitted from the object, as well as the energy reflected from the surface of the

object. If an object reflects energy from another radiating source with a higher temperature, the apparent temperature that is calculated for the object will be higher than its true temperature. Likewise, if an object reflects energy from another radiating source with a lower temperature, the apparent temperature that is calculated for the object will be lower than its true temperature.

8. Analysis of images It is important to note that our thermal imaging applications were only qualitative in nature. As starting point, to conduct a thermographic inspection of a building, it was needed to reach a minimum temperature difference of 10 C° between the interior and the exterior for several hours before the inspection begins. The measurements were both done in the evening and also in the early morning. The early morning inspections have an obvious advantage because the sunlight tends to warm exterior walls and roofs, complicating thermographic inspections. Ideally, the perfect measurement would be under conditions where sun is not shining on the building for at least three hours before the inspection begins. If the house has brick or stone veneer, the sun-free interval should be at least eight hours long. For this reason a few measurements were conducted on the same object and repeated twice, early in the morning and in the evening but keeping the same temperature difference. To ensure fairness the influence of the surrounding was considered (reflection of light, pressure difference, humidity, position and brightness of street lamps). We also found that very windy days are not good because they affect the true temperature of scanned objects. Furthermore, interior heating has to be performed enough long in order to achieve the consistency of requested temperature difference of 10 degrees Celsius between interior (indoor) and exterior (outdoor) temperature. Keeping changes to a minimal level has enabled to achieve more accurate understanding of images. In particular we tried to ensure that any known air leakage is restricted. Before doing inspection of household we intentionally produced conditions for air-leakage in order to reproduce irregular shapes with uneven boundaries mages which enable us to recognize it accurately and identify it when occurs during inspection. According to “Guidelines for Thermographic Inspections of Buildings,” a standard produced by RESNET, “The thermal image for air leakage will appear as ‘fingers’ or ‘streaking’ showing as dark when cold air is observed and lighter colors when warm air is viewed. The thermal images will produce irregular shapes with uneven boundaries and large temperature variations. These air leakage sites are often at joints, junctions or penetrations in the enclosure. There is often a temperature gradient within a finger or streaking area”.



Another important knowledge we gained was the understanding how to distinguish thermal bridging sites from thermal bypass or air leakage sites. Thermal bridging sites will not change size or shape during an inspection. We also learn that leaks in a low-slope roof can be well identified through thermal imaging. Importantly, for a useful thermographic inspection, the roof must be dry, otherwise the imaging is biased. When the sun shines on a low-slope roof, it heats up the roofing and the top of the insulation below. Once the sun sets, the insulation begins to cool; however, damp insulation cools at a much slower rate than dry insulation. That’s why leaky areas of the roof show up as warm spots when viewed at night.

In what follows, thermal Images that correspond to three household’s outdoor inspections are interpreted. The images in Figures 7, 8 and 9 correspond to the first

inspected object, images in Figures 10 and 11 correspond to the second inspected object and the images in Figure 12 correspond to the third household.

Figure 7a corresponds to the front elevation. Despite the poor resolution we were able to interpret the images. At the very initial step we thought that the horizontal cool band label as A represents the concrete floor slab which appears cooler than the rooms above and below. That this slab (and the cooler end wall on the left above A) can be ‘seen’ thermally through the outer wall, suggests that the walls are poorly insulated and that heat is escaping from the rooms through the wall fabric. If the cavity in the wall had been insulated (it should be in such a modern building), the image suggests that the insulation has been poorly fitted. This might explain the diagonal grading in wall colours from yellow (~2.5˚C) bottom left to white (5˚ C) top right. There was a

Figure 7. a) Front elevation; b) Rear elevation (east side) [4]

Figure 8. Thermal images: a) rear elevation (west end window, outdoor view); b) lounge window (interior view) [4]



Figure 10. Images of second inspected household: Front elevation (ground and first floor) [4]

Figure 11. Rear elevation; the second inspected object [4].

Figure 9. Bedroom window (interior view) [4]



Figure 12. Rear elevation, third inspected object [4] suggestion that the window frames (B) are conducting more heat out than the glass panes. The wall temperature reaches a maximum (i) above the window and (b) in the corner where the gable end juts out (C). This may be a corner effect (perpendicular walls reinforcing emission) or may represent a thermal gradient in front room of inspected flat. However, a closer inspection into the building showed that there was an error introduced in our initial understanding of the thermal image. The previous interpretation of the front elevation image assumed that the wall is brick-faced from top to bottom, concealing an internal concrete floor slab what was shown as wrong assumption. However, the floor of the considered flat corresponds with an external concrete feature on the outside of the inspected building. Thus the horizontal green band labelled on the image as A (in Figure 7a) could represent the concrete facing here. Nevertheless, our further investigation brought another difficulty in interpretation of image. Namely, the emissivity tables show that concrete and red brick have similar emissivity (0.94 and 0.93 respectively) so the previous arguments may still apply, though things are not quite as simple as we thought at the beginning. Figure 7b corresponds to rear elevation and shows an image of window on the east end side of household. It is obvious that thermal

emission is most intense at the top of the window (D). Another closer inspection has shown that there were trickle vents that could be closed to retain more heat. Additionally the frame was poorly fitted. As in the front, the frame conducts more heat than the glass panes.

Figure 8a (left) corresponds to rear elevation and shows the side of flat with west end window. One side of the window is emitting more heat at the top (E) than the other. No sign of the floor effect was found in this picture. Figure 8b corresponds to the lounge window (interior view). The cool frame was suggesting heat loss. However, glass may be reflecting warm temperatures from wall or curtain. When scanning target in order to minimize the reflection we did a few control tests changing the angle direction towards the inspected target. This way we have developed a useful methodology for reducing the possible systematic errors.

Figure 9 (interior view) reflects the heat escaping from a bedroom. With a poorly insulated wall, heat loss will be maximised at the crossing point where two walls (or wall and poorly insulated ceiling) meet. This fact is well seen here. There seems to be a 2˚C difference in interior wall temperature between centre and edge of wall. These cool corners could attract condensation and



possible mould. Furthermore, wherever two or more of the surfaces of a structure come together, such as at the top of the foundation, band joists between floors (as applicable), interior and exterior wall intersections, walls and ceilings, walls and floors, there are gaps which act as thermal bypasses. Finally, we present images of other two households (Figures 10–12) that have been captured outside during winter night.

9. Conclusions The analyses presented in this paper were carried out and interpreted by Low Carbon Community group members who despite being trained in thermal imaging are not professional experts. Whilst participating in the community project the author of this paper found thermal imaging a very powerful educational tool for teaching the concepts such as thermal conductivity, reflection and emission of infrared radiation. Furthermore it can efficiently help visualisation of all learning steps involved in the student’s process of constructing the knowledge. It also fosters self-reflected and self-directed learning and the knowledge crafting with artefacts. In particular, it helps resolving the misconception and understanding how inaccurate the representation of the image is driven by lack of knowledge (interaction of radiation with matter and interfering effects from surrounding environment).

The method of problem solving used for identifying the root causes of faults or problems associated to uncertainties is investigated. A certain set of artificial interferences of inspected object to its surrounding was artificially created or existed modified. Some items were located close to the inspected object and their impact on energy loss interpretation (reflectivity, ambient temperature) was explored. Furthermore, air flow and changes in the ambient temperature and the humidity were artificially created in order to examine the uncertainties and errors in image interpretations. Thus an impressive amount of thermal images corresponding to simulated surrounding effects were stored in a data pool (data base) for further development of pattern recognition and neural network. The modelling simulations can be an important tool for optimizing the energy loss.

Acknowledgements The author whishes to acknowledge R. Gill and Low Carbon Headington whose engagement and interest in conducing the thermal imaging household’s survey made results presented in this paper possible.

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