Building envelope modeling calibration using aerial thermography
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Construction & Building Technology↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Short-circuit current density imaging of crystalline silicon solar cells via lock-in thermography: Robustness and simplifications
Spatially resolved determination of solar cell parameters is beneficial for loss analysis and optimization of conversion efficiency. One key parameter that has been challenging to access by an imaging technique on solar cell level is short-circuit current density. This work discusses the robustness of a recently suggested approach to determine short-circuit current density spatially resolved based on a series of lock-in thermography images and options for a simplified image acquisition procedure. For an accurate result, one or two emissivity-corrected illuminated lock-in thermography images and one dark lock-in thermography image have to be recorded. The dark lock-in thermography image can be omitted if local shunts are negligible. Furthermore, it is shown that omitting the correction of lock-in thermography images for local emissivity variations only leads to minor distortions for standard silicon solar cells. Hence, adequate acquisition of one image only is sufficient to generate a meaningful map of short-circuit current density. Beyond that, this work illustrates the underlying physics of the recently proposed method and demonstrates its robustness concerning varying excitation conditions and locally increased series resistance. Experimentally gained short-circuit current density images are validated for monochromatic illumination in comparison to the reference method of light-beam induced current.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Depth Thermography: Noninvasive 3D Temperature Profiling Using Infrared Thermal Emission
Here, we introduce a technique based on infrared thermal emission, termed depth thermography, that can remotely measure the temperature distribution beneath the surface of certain objects. Depth thermography utilizes the thermal-emission spectrum in the semitransparent spectral region of the target object to extract its temperature as a function of depth, in contrast with conventional thermography, which uses the spectrally integrated thermally emitted power to measure the surface temperature. Coupled with two-dimensional imaging, for example using an infrared hyperspectral camera or scanning a single-pixel spectrometer, this technique can yield volumetric temperature distributions. Furthermore, we carried out a proof-of-concept experiment on an asymmetrically heated fused-silica window, extracting the temperature distribution throughout the sample. Depth thermography may enable noncontact volumetric temperature measurements of microscopic objects such as multilayer electronic devices or macroscopic volumes of liquids and gases, as well as simultaneous all-optical measurements of optical and thermal properties of materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Infrared thermography for laser-based powder bed fusion additive manufacturing processes
Additive manufacturing (AM) has the potential to revolutionize discrete part manufacturing, but improvements in processing of metallic materials are necessary before AM will see widespread adoption. A better understanding of AM processes, resulting from physics-based modeling as well as direct process metrology, will form the basis for these improvements. Infrared (IR) thermography of AM processes can provide direct process metrology, as well as data necessary for the verification of physics-based models. We review selected works examining how IR thermography was implemented and used in various powder-bed AM processes. This previous work, as well as significant experience at the National Institute of Standards and Technology in temperature measurement and IR thermography for machining processes, shapes our own research in AM process metrology with IR thermography. We discuss our experimental design, as well as plans for future IR measurements of a laser-based powder bed fusion AM process.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Spatially resolved determination of the short-circuit current density of silicon solar cells via lock-in thermography
We present a spatially resolved method to determine the short-circuit current density of crystalline silicon solar cells by means of lock-in thermography. The method utilizes the property of crystalline silicon solar cells that the short-circuit current does not differ significantly from the illuminated current under moderate reverse bias. Since lock-in thermography images locally dissipated power density, this information is exploited to extract values of spatially resolved current density under short-circuit conditions. In order to obtain an accurate result, one or two illuminated lock-in thermography images and one dark lock-in thermography image need to be recorded. The method can be simplified in a way that only one image is required to generate a meaningful short-circuit current density map. The proposed method is theoretically motivated, and experimentally validated for monochromatic illumination in comparison to the reference method of light-beam induced current.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Quantification of Porosity in Ceramic Matrix Composites Using Thermography
Thermography is one of the Non-Destructive Evaluation methods used to qualitatively estimate the amount of porosity in as-manufactured ceramic matrix composite (CMC) parts. Through-transmission (TT) thermography allows for comparison of thermal diffusivity caused by differences in material density and porosity at different locations in the part. In this study, volume fraction of pores and its distribution in CMC samples containing different amounts of porosity was quantified using optical microscopy and X-ray computed tomography scans. TT thermography was done on the same specimens and images were analyzed to determine the distribution of thermal diffusivity. Comparison of porosity measurements and thermal diffusivity images shows an inverse correlation. A simple two-phase analytical model describing this relationship is derived for composites and its solution is shown to compare favorably with experimental data. In the absence of porosity data, the model can be used to estimate porosity directly from thermal diffusivity data.
36 MATERIALS SCIENCE↗
Application of IR Thermography with Thermal Diffusivity Analysis for Detection of Plies Displacement in CFRP Composites
This paper presents an efficient nondestructive testing (NDT) method for detection of plies displacement in carbon fiber reinforced plastic (CFRP) composites using the active thermography technique. To assess the capability of the proposed technique for detection of plies displacement, the CFRP composite specimens with simulated fault were designed and manufactured. The IR thermography measurements were conducted using two-sided experimental arrangement. From recorded sequences of thermal images, the temperature variations versus time plots were extracted to obtain the maximum thermal contrasts. For quantitative interpretation of the results, the thermal diffusivity values for each composite material were determined and related to the fiber volume fraction. The experimental results showed that the thermal diffusivity increases with an increase of fiber volume fraction; further, the values of thermal diffusivity are specimen thickness dependent. The obtained values of thermal diffusivity varied from 1.5 × 10{sup −7} m{sup 2}/s (for 3.2 mm composite with 8.5 vol.% carbon fiber) to 2.6 × 10{sup −7} m{sup 2}/s (for 7.7 mm composite with 34.1 vol.% carbon fiber). On the basis of these results, it was concluded that the active thermography combined with thermal diffusivity analysis can be considered as a reliable NDT technique for detection of plies displacement in CFRP composites.
36 MATERIALS SCIENCE↗
Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images
Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.
36 MATERIALS SCIENCE↗
Comparison of pyrometry and thermography for thermal analysis of thermite reactions
This article examines the thermal behavior of a laser ignited thermite composed of aluminum and bismuth trioxide. Temperature data were collected during the reaction using a four-color pyrometer and a high-speed color camera modified for thermography. The two diagnostics were arranged to collect data simultaneously, with similar fields of view and with similar data acquisition rates, so that the two techniques could be directly compared. Results show that at initial and final stages of the reaction, a lower signal-to-noise ratio affects the accuracy of the measured temperatures. Both diagnostics captured the same trends in transient thermal behavior, but the average temperatures measured with thermography were about 750 K higher than those from the pyrometer. This difference was attributed to the lower dynamic range of the thermography camera’s image sensor, which was unable to resolve cooler temperatures in the field of view as well as the photomultiplier tube sensors in the pyrometer. Overall, while the camera could not accurately capture the average temperature of a scene, its ability to capture peak temperatures and spatial data make it the preferred method for tracking thermal behavior in thermite reactions.
36 MATERIALS SCIENCE↗
Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images
Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Analysis of fast-ion losses measured in MAST-U via infrared thermography and a Fast Ion Loss Detector
Fast-ion losses need to be monitored to avoid damage to plasma facing components. In existing experimental devices, the scintillator-based fast-ion loss detector (FILD) is the most advanced diagnostic for measuring fast-ion losses. However, FILDs provide only local information about the losses. Infrared (IR) thermography can be used as a complementary tool for more global monitoring of the deposition of fast-ion losses on the wall, at the expense of no velocity-space resolution. IR cameras measure the temperature of the plasma facing components. This measurement, determined by a combined effect of the thermal plasma, radiation, neutrons and fast-ion losses, can be decomposed to infer the fast-ion load on the tokamak wall. In this manuscript, a workflow to estimate fast-ion losses via IR thermography is applied to the MAST-U spherical tokamak, using a 1D approximation to extract the experimental heat flux on the FILD front face from IR data. To numerically estimate the different contributions to this total heat flux, the field-line tracing environment SMITER is used to calculate the thermal plasma contribution, the orbit-following Monte-Carlo code ASCOT to estimate the fast-ion losses, and bolometry measurements for the radiation. To validate the workflow, two discharges, L-mode plasmas with low MHD activity, were executed using on and off-axis beams, respectively. The experimentally and numerically estimated heat flux are of the same order of magnitude for the on-axis heated scenario, with a strong dependence of the estimated fast-ion losses contribution on the fit to the kinetic profiles used as input. This is also true for the off-axis heated scenario, where the total numerically estimated heat flux is 2.1 or 1.3 times higher than the maximum experimentally estimated heat flux, depending on the ASCOT input used.
FILD↗
Building envelope anomaly characterization and simulation using drone time-lapse thermography
Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images
In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.
Zhang, Xin↗
Millikelvin-resolved ambient thermography
Thermography detects surface temperature and subsurface thermal activity of an object based on the Stefan-Boltzmann law. Impacts of the technology would be more far-reaching with finer thermal sensitivity, called noise-equivalent differential temperature (NEDT). Existing efforts to advance NEDT are all focused on improving registration of radiation signals with better cameras, driving the number close to the end of the roadmap at 20 to 40 mK. In this work, we take a distinct approach of sensitizing surface radiation against minute temperature variation of the object. The emissivity of the thermal imaging sensitizer (TIS) rises abruptly at a preprogrammed temperature, driven by a metal-insulator transition in cooperation with photonic resonance in the structure. The NEDT is refined by over 15 times with the TIS to achieve single-digit millikelvin resolution near room temperature, empowering ambient thermography for a broad range of applications such as in operando electronics analysis and early cancer screening.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Effect of Loading Frequency on Fatigue Behavior of Additively Manufactured Short Fiber Thermoplastics via Infrared Thermography
Fracture, Fatigue, Failure, Damage Evolution and Thermomechanics & Infrared Imaging, Volume 4 of the Proceedings of the 2025 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the fourth volume of five from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of areas, including: • 3D Deformation and Failure • In-situ Techniques and Microscale Effects on Mechanical Behavior • Vibration and Cyclic Fatigue • Integration of Models and Experiments • Interface-Mediated Damage and Failure • Fracture and Fatigue in Brittle Materials • Material Characterizations Using Thermography • Fatigue, Damage & Fracture Evaluation Using Infrared Thermography
Pathak, Pharindra [Auburn University]↗
Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning
The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Defect Detection in Fuel Cell Gas Diffusion Electrodes Using Infrared Thermography
Polymer electrolyte membrane fuel cells are energy conversion devices that offer high power densities and high efficiencies for mobile and other applications. Successful introduction into the marketplace requires addressing cost barriers such as production volumes and platinum loading. For cost reduction, it is vital to minimize waste and maximize quality during the manufacturing of platinum-containing electrodes, including gas diffusion electrodes (GDEs). In this work, we report on developing a quality control diagnostic for GDEs, involving creating an ex situ exothermic reaction on the electrode surface and using infrared thermography to measure the resulting temperature profile. Experiments with a moving GDE containing created defects were conducted to demonstrate the applicability of the diagnostic for real-time web-line inspection.
30 DIRECT ENERGY CONVERSION↗
Examining infrared thermography based approaches to rapid fatigue characterization of additively manufactured compression molded short fiber thermoplastic composites
A novel additive manufacturing (AM) methodology combined with a compression molding (CM) process has been developed to optimize the microstructure of short fiber thermoplastic composites (SFTs)with higher fiber alignment and lower porosity, yielding superior stiffness, strength, and structural integrity. Here, the current work examines the efficacy of the ‘passive’ infrared thermography (IRT) techniques for rapid fatigue characterization of SFTs that use the surface temperature evolution during cyclic loading due to self-heating as a fatigue indicator. A comparison of fatigue limits obtained from traditional stress-life (SN) (≈53.1%σ uts ) and IRT (≈54.1%σ uts ) shows a close match. However, the SN curve required 18 specimens and two weeks of continuous cyclic testing, while IRT used three specimens with 5 hours of testing. Thus, the IRT approach provides an accelerated testing framework for rapidly estimating the fatigue limit. Additionally, existing phenomenological approaches to IRT fatigue characterization have been examined.
42 ENGINEERING↗