FLIR Research Articles

Digital twin-based tig welding quality prediction using electrode tip angle degradation influencing Industry 5.0 in manufacturing sector

Automation in tungsten inert gas (TIG) welding is important to achieve high production rates and quality in manufacturing industries. To improve the welding process and quality inspection methodologies, the intelligent welding robot and vision-based inspection system have been researched and deployed in many engineering fields. Hence to enhance the performance and production, a digital twin-based welding system with the prediction of weld quality based on the consideration of electrode tip angle degradation. The proposed system will capture real-time electrode tip angle and weld pool temperature using a forward looking infrared (FLIR) camera along with welding current and speed correlated with tensile strength as the output parameter. To validate the analysis, support vector machine (SVM) and random forest (RF) algorithms were implemented in which the RF model performs well on the prediction of welding quality by mapping with tensile strength. RF model confirms maximum accuracy of 90% with 0.29 seconds computation time to perform prediction on the next execution of welding operation. It is inferred that if the tip angle degradation increases consecutively welding current decreases drastically impacting the weld quality from good to poor. To forecast the need for immediate or scheduled maintenance to reduce the tip angle degradation, a linear regression algorithm is implemented to enable the inspection engineer to perform maintenance without delay in production.

Temporal Variation and Persistence of Methane Emissions from Shallow Water Oil and Gas Production in the Gulf of Mexico.

Methane emissions from the oil and gas supply chain can be intermittent, posing challenges for monitoring and mitigation efforts. This study examines shallow water facilities in the US Gulf of Mexico with repeat atmospheric observations to evaluate temporal variation in site-specific methane emissions. We combine new and previous observations to develop a longitudinal study, spanning from days to months to almost five years, evaluating the emissions behavior of sites over time. We also define and determine the chance of subsequent detection (CSD): the likelihood that an emitting site will be observed emitting again. The average emitting central hub in the Gulf has a 74% CSD at any time interval. Eight facilities contribute 50% of total emissions and are over 80% persistent with a 96% CSD above 100 kg/h and 46% persistent with a 42% CSD above 1000 kg/h, indicating that large emissions are persistent at certain sites. Forward-looking infrared (FLIR) footage shows many of these sites exhibiting cold venting. This suggests that for offshore, a low sampling frequency over large spatial coverage can capture typical site emissions behavior and identify targets for mitigation. We further demonstrate the preliminary use of space-based observations to monitor offshore emissions over time.

PENGGUNAAN SINAR INFRA MERAH UNTUK DETEKSI PANAS BUMI DAERAH SANGKANHURIP, KUNINGAN, JAWA BARAT

Geothermal resources can be explored with aerial photographs followed by surveys and analysis of geological, geochemical and geophysical data as well as shallow drilling. This research was conducted to determine the potential of geothermal resources using a camera capable of capturing infrared rays from objects and photo targets.Geothermal exploration is generally carried out with an initial survey using remote imagery and aerial photography techniques. Taking photos using a tool equipped with a Thermal Infrared Sensor (TIRS), namely a camera equipped with Forward Looking InfraRed (FLIR).The initial survey in this study was conducted at five locations of geothermal springs in the Sangkanhurip area, Kuningan, West Java. From these five locations using FLIR cameras detected geothermal resources with hot spring temperatures between 30 to 40 degrees Celsius.To find out the types of rocks that exist, a geological survey was carried out to make a geological map. The data and information obtained are then analyzed to obtain a map that displays geology with surface temperature data in the study area.This map can be utilized by companies interested in exploring and producing geothermal energy for use as a source of energy for Geothermal Power Generation (PLTP). Further analysis can be carried out with the Geochemical Method for predicting subsurface temperature. This discovery fulfills the research objective of detecting geothermal resources using infrared rays.

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8.

With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management.

TIR‐YOLO‐ADAS: A thermal infrared object detection framework for advanced driver assistance systems

AbstractAn object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night‐time and in low‐visibility conditions. The proposed detection framework, referred to as TIR‐YOLO‐ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward‐looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR‐YOLO‐ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.

The correlation between transcutaneous oxygen pressure (TcPO2 ) and forward-looking infrared (FLIR) thermography in the evaluation of lower extremity perfusion according to angiosome.

The increased peripheral arterial disease (PAD) incidence associated with aging and increased incidence of cardiovascular conditions underscores the significance of assessing lower limb perfusion. This study aims to report on the correlation and utility of two novel non-invasive instruments: transcutaneous oxygen pressure (TcPO2 ) and forward-looking infrared (FLIR) thermography. A total of 68 patients diagnosed with diabetic foot ulcer and PAD who underwent vascular studies at a single institution between March 2022 and March 2023 were included. Cases with revascularization indications were treated by a cardiologist. Following the procedure, ambient TcPO2 and FLIR thermography were recorded on postoperative days 1, 7, 14, 21 and 28. In impaired limbs, TcPO2 was 12.3 ± 2 mmHg and FLIR thermography was 28.7 ± 0.9°C. TcPO2 (p = 0.002), FLIR thermography (p = 0.015) and ankle-brachial index (p = 0.047) values significantly reduced with greater vascular obstruction severity. Revascularization (n = 39) significantly improved TcPO2 (12.5 ± 1.7 to 19.1 ± 2.2 mmHg, p = 0.011) and FLIR (28.8 ± 1.8 to 32.6 ± 1.6°C; p = 0.018), especially in severe impaired angiosomes. TcPO2 significantly increased immediately post-procedure, then gradually, whereas the FLIR thermography values plateaued from day 1 to 28 post-procedure. In conclusion, FLIR thermography is a viable non-invasive tool for evaluating lower limb perfusion based on angiosomes, comparable with TcPO2 .

75 Intraoperative Use of Infrared Thermal Imaging Camera – a Clinical Aid for Pedicled Flaps in Hand Reconstruction

Abstract Introduction Venous insufficiency remains one of the commonest causes for flap failure. Forward-looking infrared (FLIR) thermography measures infrared emissions from dissipated heat and converts temperature values into an image. This has various advantages to the current use of indocyanine green (ICG) angiography to assess flap perfusion. Case A man in his mid-30s was involved in a road traffic accident and sustained extensive injuries to his right hand. These included multiple metacarpal fractures, degloving of the proximal aspect of the entire thumb and significant skin loss on the volar distal forearm. After several washouts and tissue debridement, a pedicled chest wall flap was performed. Five weeks later the chest wall flap was divided using an infrared thermal imaging camera (FLIR E5) intraoperatively as a clinical aid to assess perfusion. This detected a small area of venous congestion which was not detectable by clinical assessment. Discussion There are drawbacks to the current intraoperative use of ICG angiography to assess flap perfusion. These include hot flushes and skin rashes, and the technique requires an intraoperative injection, extensive equipment involving a screen and a specialised camera. In comparison FLIR cameras are handheld, easy to use and involves a one-off cost of purchasing the camera that is relatively inexpensive compared to ICG angiography. Conclusions Thermal imaging techniques such as FLIR provides an alternative intraoperative option for patients who are unable to have ICG, for centres that do not have access to ICG and for surgeons looking for more convenient alternatives.

Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu'um Geothermal Manifestation Exploration Security

Geothermal energy is a viable alternative energy source, particularly in Indonesia. This study was conducted at Ie Seu’um, Mount Seulawah Agam, which is a potential site for a geothermal power plant with an estimated electrical output of 150 megawatts. The objective of this study was to analyze and construct a digital elevation model (DEM) map of the geothermal manifestations. We analyzed water temperature, FLIR (Forward Looking Infrared) temperature, and temperature data from Landsat 8 satellite imagery. To map the heat signature of geothermal features, we utilized the DJI Phantom 4 Standard equipped with the FLIR One Gen 2 sensor. Additionally, we used the Milwaukee Mi306 to calculate the water temperature. Each test was conducted three times to obtain an optimal average level of accuracy. The DEM map was created to assess the level of safety in geothermal manifestation exploration. Elevation and slope values were analyzed to generate a 3D map display, providing a clearer image of the research site. In conclusion, drones prove to be an excellent method for ensuring the safety of exploration in geothermal manifestation areas.

Evaluation of Range Estimation Performance of FLIR with Field Requirements Criteria

Thermal imaging performance depends on many variables, ranging from the properties of the imaged object to atmospheric transmittance and system parameters. After clarification of the functional needs in system design or procurement, system parameters of the design that can meet these needs should be determined. Diagnosis/recognition from a distance is one of the foremost of these needs. The following briefly introduces the Forward Looking Infrared (FLIR) systems, followed by explanations for calculating the theoretical diagnostic range. After the theoretical information, sample systems are given, and high-performance FLIR systems are presented. To accurately analyze, measure and predict the performance of FLIR systems, a model should calculate summary performance measures of the system in the form of Minimum Resolvable Temperature (MRT) and Modulation Transfer Function (MTF) between a target and its background and estimate range for a given scenario electro-optical required for the performance evaluation of the system. The accuracy of these calculations will ultimately determine the accuracy of the model by which the performance of the FLIR system is evaluated.

Influence of circadian rhythm, breed, stage of lactation, milk yield and parity on body and udder skin surface temperature of lactating cows monitored by infrared thermography

ABSTRACT The objective of the study was to establish baseline thermographic information on body and udder skin surface temperature (USST) of lactating cows in different stages of lactation, milk yield, parity, breed and season. Holstein Friesian crossbred (n = 19 cows) and Deoni lactating cows (n = 14 cows) were monitored for body (i.e. eye) and USST prior to milking using a forward looking infra-red (FLIR) camera. It was observed that the mean body and USST of both crossbred and Deoni cows did not differ significantly. The body and USST of both the breeds were significantly higher by 0.9–1.0°C during evening than morning milking. There was no difference in body and USST between days and between udder quarters. Similarly, stage of lactation, milk yield and parity did not show any influence over body and USST. The body and USST were higher in summer (1.1°C) than in spring and winter seasons. Deoni cows had 1.0°C lesser body and USST than crossbred cows. It is concluded that baseline thermographic information on body and USST would be useful in developing breed-specific thermographic signature for individual animal.

DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data.

Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96-99.98% classification accuracy with fewer parameters.

Small Scale Aerial Monitoring for Human Body Temperature Measurement Using Rotary Wing Drone

In Indonesia, the COVID-19 pandemic has had an impact on a variety of sectors. Using all available technology for disaster mitigation is critical for pandemic prevention and control. Recent studies have uncovered the advantage of Unmanned Aerial Vehicle (UAV) or drones, particularly those with rotary wings, in dealing with the pandemic. Much effort has been devoted to developing a rotary-wing drone system as a flying platform for aerial monitoring. However, several factors must be considered when visually observing a specific region, i.e., the area's size, topographic contours, locations of special interest inside the area, approach points to the area, and the observation timeframe. Since fever is a common symptom of COVID-19, human body temperature monitoring is highlighted for fever screening, with the objective of minimizing people with high body temperatures going to the crowd. A major challenge is creating a system that can provide accurate body temperature data, which is critical for fighting the pandemic. The purpose of this paper is to present a rotary wing drone application for aerial human body temperature measurements. The paper also proposed an alternative solution based on using a portable, low-cost, Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the drone's electronic system. Furthermore, thermal image data are transmitted into the ground station via a radio telemetry transceiver to allow flexible surveillance by the operator. Indoor and outdoor experiments reveal that the system has been effectively installed and provide data collection for further research. The results show that the system can be used for small-scale area aerial monitoring.

Digital infrared thermal imaging of body and hoof skin surface temperature profile in Murrah buffaloes (Bubalus bubalis): A preliminary report

The aim of the present study was to generate thermographic profile of foot region viz. body temperature (BTºC), coronary band temperature (CBTºC), hoof skin surface temperature (HSSTºC) and mean temperature difference (ΔTºC) in Murrah buffaloes (Bubalus bubalis) and its relation with ambient temperature. A total of 60 hooves of lactating Murrah buffaloes (n = 15) maintained in a farm, were monitored once a day continuously for 15 days before evening milking using forward-looking infrared (FLIR) i5 camera. Total of 1125 thermograms were analyzed using FLIR Quick Report 1.2 image analysis software. The mean ± SD (°C) of BT, CBT, HSST and ΔT of all animals throughout the experimental period were 37.39±0.05, 34.93±0.13, 34.16±0.13 and 0.76±0.26 respectively. The CBT was around 0.5 to 1°C higher than HSST. Statistical analysis revealed that ambient temperature is highly and positively correlated with CBT (ºC) (r = 0.95) and HSST (ºC) (r = 0.99), indicating greater influence of ambient temperature on surface temperature of foot region. Therefore, CBT (ºC) and HSST (ºC) have a tendency to follow variations in ambient temperature (R2 = 0.91 and 0.98 respectively). However, significant difference was observed between BT vs. CBT, BT vs. HSST and CBT vs. HSST (ºC). The mean ± SD (°C) of ΔT of all animals, throughout the observation period was 0.96±0.41. A significant difference was observed in ΔT between animals (P<0.001). However, significant difference was not observed in ΔT between days and among four hooves within animal. It is concluded that, to the best of our knowledge this is the first report on baseline thermographic information on BT, CBT, HSST and ΔT differentials for buffalo and its clinical significance in monitoring hoof health and management.

Multi-Model Fusion of Encoding Methods-Based Visual Words for Target Recognition in Infrared Images

The limited information contained in infrared images present a serious problem, therefore it is necessary to form a powerful feature descriptor that allows extracting the maximum information and describing the image efficiently. To address this challenge, we propose a novel approach named multi-model fusion of encoding methods (MMFEM). First, several encoding methods for Bag Of Visual Words (BOVW) model were evaluated. Then, we fuse the best encoding methods obtained using three levels of fusion: feature-level fusion, decision-level fusion and hybrid-level fusion. Finally, the outputs of the fusion process were used to form a final decision for target recognition in infrared images. Two infrared datasets were employed to evaluate the performance of the proposed approach. The first one is Visible and Infrared Spectrum (VAIS) dataset comprising six categories of ships and the second dataset is a subset of Forward-Looking InfraRed (FLIR) thermal dataset comprising two object categories, vehicles and pedestrians. The proposed approach has exceed the state of the art for both datasets and we have reached 96.96% for FLIR and 71.26% for VAIS in overall classification accuracy.

Object detection from UAV thermal infrared images and videos using YOLO models

• 15 models were trained from ground-based images and videos using YOLO networks for UAV-based TIR images and videos detection. • The best and fastest model of the YOLO series models were identified. • UAV TIR videos detection can achieve real-time performance with 40 FPS. Object detection is one of the most crucial tasks in computer vision and remote sensing to identify specific categories of various objects in images. The unmanned aerial vehicle (UAV)-based thermal infrared (TIR) remote sensing multi-scenario images and videos are two important data sources in public security. However, their object detection process is still challenging because of the complicated scene information, coarse resolution compared with the visible videos and lack of public labelled datasets and training models. This study proposed a UAV TIR object detection framework for images and videos. The You Only Look Once (YOLO) models based on Convolutional Neural Network (CNN) architecture were designed to extract features from ground-based TIR images and videos, which were captured by Forward-looking Infrared (FLIR) cameras. The most effective algorithm was finally identified by evaluation metrics and then applied to detect objects on TIR videos from UAVs. Results showed that the highest mean average precision (mAP) of the person and car instances was 88.69% in the validating task. The fastest detection speed achieved 50 frames per second (FPS), and the smallest model size was observed in YOLOv5-s. In the application, the cross-detection performance on persons and cars in UAV TIR videos under a YOLOv5-s model was discussed in terms of the different UAVs’ observation angles and the effectiveness of the YOLO architecture was revealed. This study provides positive support for the qualitative and quantitative evaluation of objection detection from TIR images and videos using deep-learning models.

Thermal history and high-speed optical imaging of overhang structures during laser powder bed fusion: A computational and experimental analysis

Laser powder bed fusion (LPBF) is a powerful tool for additive manufacturing (AM) of metal components. However, fabricating components with overhanging features using LPBF remains a challenge. Overhangs suffer from dimensional inaccuracies, high surface roughness, and agglomerated material or dross. These parts often do not meet engineering requirements and are discarded and reprinted until requirements are met, or redesigned to avoid overhangs. Printing a flat overhang is especially challenging due to the long, unsupported powder bed that fosters material agglomeration and inhibits heat flux. Commercial LPBF machines use distinct laser parameters for the downskin layers in overhang regions. Downskin parameters for a given material are often selected by trial and error: printing many parts, changing the laser power and velocity parameters, as well as the number of layers to apply them. The impact of changing the laser parameters on the thermal history of a given layer and multiple layers is not well characterized or understood. This research uses thermal simulations, infrared (IR) imaging, and high-speed optical imaging to study the fabrication of flat overhang structures in 316L stainless steel. The thermal evolution of different downskin strategies on multiple layers is simulated with high fidelity thermal simulations and experimentally measured as the part is printed with carefully calibrated forward looking infrared (FLIR) imaging. Emissivity measurements are performed in a custom chamber under a range of conditions to accurately convert FLIR measurements to temperatures. The layer construction is also recorded with high-speed video to observe the powder bed, melt pool formation, and effects of material agglomeration. Parameter strategies that minimize layer-to-layer variations of the peak temperature, heating rate, and cooling rate are investigated to inform print conditions for overhang features.

Efficacy of spotlights and thermal cameras to detect lions Panthera leo and spotted hyenas Crocuta crocuta depends on species and management regime

Accurate abundance estimates can contribute to effective management of large carnivore populations. Lion Panthera leo and spotted hyena Crocuta crocuta populations are frequently estimated at night by eliciting their approach using broadcasted vocalizations. Spotlights are typically used to observe these species on approach but can disturb animals and adversely affect counts. We compared the efficacy of spotlight with red filters and forward looking infrared (FLIR) thermal monocular to enumerate lions and spotted hyenas in Serengeti National Park (SNP; non‐hunted area) and Maswa Game Reserve (MGR; hunted area), Tanzania, during 2015–2017. We established 119 call‐in sites in SNP and 20 in MGR and conducted repeated call‐ins at 1–2 week intervals. During call‐ins we conducted systematic paired counts using both devices. We assessed the influence of device order, species, hunting regime and land cover on species counts. We found that FLIR was more efficacious for counting hyenas in MGR and spotlight for counting lions in SNP. We found evidence for temporary artificial light disturbance in MGR, as counts were higher when FLIR was used as the second device. Habitat type within 200 m of call‐in sites did not influence device performances. Greater spotlight efficacy in SNP is a likely consequence of lower perceived risk and less anthropogenic disturbance compared to MGR. To improve accuracy of counts and subsequent population estimates for lions and spotted hyenas, we recommend consideration of variation in device efficacy, based on species surveyed and management regime.

Temperature Performance of Stainless Steel AISI420B Orthopedic Drill Bits Simulation Study

In orthopedic and trauma surgery, the drilling of bone is the process of preparing, making, and enlarging a hole of circular crosswire in a solid anisotropic material bone structure. The failure of irreversible osteonecrosis occurs when the temperature rises to 47 °C or higher during bone drilling. Osteonecrosis is a disorder that leads to the death of bone tissue cells, resulting in weaker contact between implants and bone, and in some cases, the loss of strong and stable attachment to the bone. Previous studies had concentrated on drill bit material, design, point angle, force, speed, and feed rate. This study outlines the technique for developing a drilling-related finite element model to verify the drilling process’s efficacy using a computer simulation approach. Software LS-DYNA © 2011-2020 LST was used to conduct a drilling model based on the finite element (FE) technique. The temperature behavior of orthopedic chromium type martensitic magnetic stainless steel AISI420B twist drill bit (Ø 4.3 mm) was determined. The findings were quantified, and the outcomes of the simulated and actual evaluations were compared. The surgeon’s typical drilling technique or pattern was used in this study, with a penetration angle of no more than 30°, and performed using a battery-powered surgical hand drill. A forward-looking infrared (FLIR) camera for temperature detection was used to record drill bits thermal data throughout the experiment. The utilization of two factorial analyses in the design of experiment (DOE) and analysis of variance (ANOVA) revealed that the simulation result had higher mean performance confirmation than the actual experimental result, with a difference of 10.9102%. The AISI420B showed good heat conductivity, and minor wear than typical stainless steel drill bits in the market for bone drilling. Therefore, the AISI420B stainless steel twist drill bit tested able to perform well for bone drilling.

Postoperative Detection of Free Flap Congestion in a Fitzpatrick Skin Type VI Patient using the Flir Thermal Imaging Camera: A Case Report and Literature Review

BACKGROUND: Free flaps are routinely used in complex tissue reconstruction due to their functionality and reliability. Postoperative monitoring remains a challenge despite the current available modalities. Clinical examination remains the gold standard, with color being the most sensitive marker of flap compromise. Assessment of flap color is more challenging in Fitzpatrick V-VI skin types, masking visual signs of ischemia or congestion. A Forward-Looking Infrared (FLIR) ONE smartphone based thermal imaging camera can be used to detect differences in flap temperature from the surrounding native tissue and could be used to identify early flap compromise. This simple technology used with a smartphone may be a useful method to assess postoperative flap perfusion. METHODS: Institutional review board approval was obtained for postoperative flap monitoring using FLIR technology for patients undergoing complex reconstruction with a free anterolateral thigh (ALT) flap. The FLIR camera is a simple attachment that plugs into iPhone models 7–12 and takes thermal pictures with temperature readings by a spot pyrometer using their App. Preoperatively the FLIR camera spot pyrometer measured baseline temperature by ALT flap location. Temperature recordings of the flap and the surrounding native tissue were taken immediately postoperatively and then at regular intervals in addition to our standard free flap monitoring protocol. This protocol was utilized for one patient in this report who underwent a free ALT flap to scalp after sarcoma resection and Fitzpatrick skin type VI. RESULTS: FLIR thermography measured the preoperative central flap temperature at 32.6°C. Immediately postoperatively the flap temperature was 33.9°C, and the surrounding native skin was 35.8°C. Sixteen hours postoperatively the central portion of the flap was found to be 28.0°C, 8.4°C cooler than the surrounding native skin, suggesting flap ischemia. Clinical examination of the flap showed edema and return of dark blood on scratch test but no frank discoloration. Handheld Doppler signal showed arterial signal but no venous signal. The patient was taken immediately for operative exploration, which showed a 30 cm3 hematoma compressing the vascular pedicle. Following evacuation, the central flap’s temperature was 35.6°C. The patient was discharged on POD 7 and still has a complete reconstruction. CONCLUSIONS: FLIR ONE was helpful in detecting flap congestion and ultimately flap salvage. Prompt operative evacuation of the hematoma prevented flap loss and associated morbidity to the patient. This patient case highlights the inherent challenges in evaluating skin paddles of Fitzpatrick V–VI skin types and depicts the utility of a low-cost thermography camera that can aid in identifying a threatened flap. The user-friendly, non-contact nature of FLIR ONE adds a useful and objective datapoint in postoperative free flap monitoring that can improve patient outcomes when combined with conventional monitoring techniques in all patients, particularly those with difficult flaps to monitor. Our team hopes to continue studying thermal camera temperature differences in postoperative free flap monitoring to service our patients and provide insight into this technology’s utility for reconstructive plastic surgery.

Spontaneous reheating of crystallizing lava

Abstract We show that recalescence, or spontaneous reheating of a cooling material due to rapid release of latent heat, can occur during disequilibrium crystallization of depolymerized Mg-rich melts. This can only happen at fast cooling rates, where the melt becomes undercooled by tens to hundreds of degrees before crystallization begins. Using a forward-looking infrared (FLIR) camera, we documented recalescence in pyroxene (Fe, Mg)SiO3 and komatiite lavas that initially cooled at 25–50 °C s–1. Local heating at the crystallization front exceeds 150 °C for the pyroxene and 10 °C for komatiite and lasts for several seconds as the crystallization front migrates through. We determined the latent heat release by differential scanning calorimetry to be 440 J g–1 for pyroxene and 275 J g–1 for komatiite with a brief power output of ∼100 W g–1 or ∼300 MW m–3. Recalescence may be a widespread process in the solar system, particularly in lava fountains, and cooling histories of mafic pyroclasts should not be assumed a priori to be monotonic.