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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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↗
DOE OSTI2025

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]↗
DOE OSTI2024

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↗
DOE OSTI2023

Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy

Infrared thermographs (IRTs) are commonly used during disease pandemics to screen individuals with elevated body temperature (EBT). To address the limited research on external factors affecting IRT accuracy, we conducted benchtop measurements and computer simulations with two IRTs, with or without an external temperature reference source (ETRS) for temperature compensation. The combination of an IRT and an ETRS forms a screening thermograph (ST). We investigated the effects of viewing angle (θ, 0–75°), ETRS set temperature (T ETRS , 30–40 °C), ambient temperature (T atm , 18–32 °C), relative humidity (RH, 15–80%), and working distance (d, 0.4–2.8 m). We discovered that STs exhibited higher accuracy compared to IRTs alone. Across the tested ranges of T atm and RH, both IRTs exhibited absolute measurement errors of less than 0.97 °C, while both STs maintained absolute measurement errors of less than 0.12 °C. The optimal T ETRS for EBT detection was 36–37 °C. When θ was below 30°, the two STs underestimated calibration source (CS) temperature (T CS ) of less than 0.05 °C. The computer simulations showed absolute temperature differences of up to 0.28 °C and 0.04 °C between estimated and theoretical temperatures for IRTs and STs, respectively, considering d of 0.2–3.0 m, T atm of 15–35 °C, and RH of 5–95%. The results highlight the importance of precise calibration and environmental control for reliable temperature readings and suggest proper ranges for these factors, aiming to enhance current standard documents and best practice guidelines. These insights enhance our understanding of IRT performance and their sensitivity to various factors, thereby facilitating the development of best practices for accurate EBT measurement.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Relating Aerial Infrared Thermography Defects to Photovoltaic Performance: Preprint

In this research, we examine the relationship between aerial IR defect analysis and photovoltaic (PV) performance data for twelve utility- and commercial-scale solar sites in the United States. To do this, we fuse the site diagram geoJSON's, aerial infrared thermography (aIRT) defect analyses, and associated inverter time series, allowing for a direct comparison between site defects and time series data. Defect analyses were provided by Zeitview, under its Solar Insights platform. Following the data fusion process, we look at the relationship between system performance and aIRT defects. We investigate the relationship between degradation and hotspot defects, as well as the relationship between AC power data and offline strings and misaligned modules. In general, system degradation was not affected by long-term or balance-of-system (BoS) defects as they occurred infrequently in the data set. However, for one system, a near statistically significant relationship (p-value=0.057) was found when comparing the degradation of inverter blocks with several multi-hotspot defects to all other inverter blocks without this particular defect. There was strong alignment when comparing short-term recoverable module defects such as stuck trackers and offline strings to time series data. In general, we found that when an inverter block has more than 80% of modules flagged for one of these defects, its AC power time data is flat-lined and the inverter block is not producing.

aerial inspection↗
DOE OSTI2020

Real-time cure monitoring of fiber-reinforced polymer composites using infrared thermography and recursive Bayesian filtering

Despite the desirable characteristics of fiber-reinforced polymer (FRP) composites, their utilization in high volume production industries is limited by a lack of efficient manufacturing techniques. Monitoring the curing process of these composites can help with improving the quality and efficiency of the manufacturing process. This article discusses a method that uses Kalman-filter-based fusion of the information obtained from infrared thermography (surface temperature measurements) and a heat conduction model to estimate degrees of cure and internal temperatures of curing FRP composite parts in real time. The effectiveness of the methodology is demonstrated by successfully monitoring the curing of an FRP composite part in a laboratory experiment. Finally, the proposed methodology is a crucial step towards identifying anomalies in the curing process that negatively impact the quality of FRP composite parts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2022

Quantifying deformation during Zry-4 burst testing: a comparison of BISON and a combined in-situ digital image correlation and infrared thermography method

The development and verification of nuclear fuel-cladding performance codes require extensive testing to establish empirical correlations, necessitating more accelerated testing methods that can replace large validation studies. Here, in the present work, a technique was developed and implemented to experimentally quantify the relationship between temperature, cladding internal pressure, and strain in-situ during a burst test, with the specific aim of generating data for code validation and model refinement. Digital image correlation was used to measure hoop, axial, and radial strain, and infrared thermography to quantify axial temperature gradients. Several key experimental modifications to traditional LOCA burst testing were necessary, but are shown to effectively have little impact on burst temperatures. The time/temperature dependent pressure and time/spatially dependent thermal gradient data were used with BISON to simulate cladding burst and strain rates. Although the measured DIC strains reach appreciable amounts at slightly lower temperatures than BISON simulated strains, there is good agreement between the measured and simulated strains in terms of magnitude and rates leading up to burst. However, in the few seconds prior to and during burst, the BISON simulated strains are significantly lower than the measured strains, indicating the potential for model improvement. The combined experiment and simulation technique may be applied to any new cladding concept to accelerate fuel qualification.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In-situ thermomechanical measurements during burst testing of Zr claddings using digital image correlation and infrared thermography techniques

Burst testing has long been a relied-on method for assessing fuel cladding materials in light-water reactoraccident scenarios. However, burst testing historically provided minimal in-situ observations beyond a few thermocouple readings, pressure from transducers, and post-test deformation measurements. Acquisition of full cladding thermomechanical data throughout a burst test would be invaluable to fuel system qualification, material model development and fuel performance code validation, such as the BISON fuel performance code. Recently, methodologies for simultaneously applying 2-dimensional digital image correlation and infrared thermography techniques to quantify the relationship between temperature, internal cladding pressure, and deformation during burst testing were developed. This work details results from the application of these techniques to burst testing of Zircaloy-4 cladding segments subjected to loss-of-coolant accident transients in an air environment. Two types of tests were performed with: (a) cladding segments connected to a fixed pressure reservoir as well as (b) closed systems where internal pressure increased during transients. In-situ thermomechanical measurements including strain and thermal gradient data are presented, demonstrating the effectiveness of applying combined surface measurement techniques to burst testing

Bell, Sam↗
DOE OSTI2025

Pulsed Infrared Thermography Nondestructive Imaging of SiC-SiC f Composite Cladding Architectures; Understanding the Performance of SiC-SiC f Composite Cladding Architectures with Cr Coating in Normal Operating and Accident Conditions in LWRs and Advanced Reactors

SiC-SiC f composites, consisting of silicon carbide fibers embedded in a silicon carbide matrix, are advanced materials with high thermal conductivity, temperature stability, and resistance to radiation damage. Traditional methods for quality control of fabricated SiC-SiC f composites involve nondestructive evaluation (NDE) with X-ray computed tomography (XCT). However, XCT imaging of typical SiC-SiC f structures for cladding applications can involve several hours. In this project, we investigate an alternative approach to NDE of SiC-SiC f composites that involves rapid (on the order of seconds) imaging with Pulsed infrared thermography (PIT). PIT images of a planar SiC-SiC f specimen show the structure of the surface monolith layer and internal SiC f structures. The capability of PIT imaging in visualizing SiC f structures is qualitatively confirmed by observing similarity in the PIT and X-ray transmission images of the same specimen. Computer vision analysis of defects in the PIT image of the monolith was performed with thresholding followed by topological structural analysis that computed geometric descriptors, including major/minor axes of fitted ellipses, area, perimeter, Feret diameter, circularity, roundness, and solidity.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

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↗
DOE OSTI2026

Infrared thermography NDT for in-situ defect detection in sandwich composite panel manufacturing

Composite manufacturing presents numerous challenges, as defects can arise from various sources throughout the process. In sandwich composite structures, the integration of a foam core introduces additional complexity and increases the likelihood of defect formation like delamination. To mitigate these issues and reduce the risk of future structural failures, in-situ monitoring during manufacturing is essential. This study investigates infrared (IR) thermography as a non-destructive technique for detecting manufacturing defects in foam-core sandwich composite panels under thermally excited conditions representative of in-situ processing. A stationary FLIR A8590 IR camera (640 × 512 pixels, 30Hz, 17mm lens, 9 ft stand-off distance) was used to monitor prefabricated panels subjected to controlled external heating simulating compression molding and resin cure exotherm. Interlaminar delamination defects with characteristic sizes ranging from 0.25 × 0.25in² to 5 × 5in² produced measurable surface temperature depressions of approximately 4–10°C during transient cooling, exceeding the effective noise floor of the camera by more than two standard deviations. Thicker laminates exhibited prolonged defect detectability windows due to increased thermal diffusion time. In contrast, embedded Teflon inclusions generated weak thermal contrasts of ≤ 3°C, approaching the measurement noise floor, due to limited thermal property contrast with the surrounding glass fiber composite. These results establish quantitative detectability limits for stationary thermographic inspection of sandwich composite panels under manufacturing-representative thermal cycles.

Barakat, Abdallah [ORNL] (ORCID:0000000296141398)↗