Search NASA⌕ Search

SEARCH · Search NASA

Results for “non-destructive inspection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Shearographic Non-destructive Evaluation of Space Shuttle Thermal Protection Systems

Preliminary results of shearographic inspections of the shuttle external tank (ET) spray-on foam insulation (SOFI) and solid rocket booster (SRB) Marshall sprayable ablative (MSA-2) epoxy-cork thermal protection systems (TPS) are presented. Debonding SOFI or MSA-2 damage the orbiter 'belly' tile and exposes the ET/SRB to thermal loading. Previous work with the ET/SRB showed promising results with shearography. The first area investigated was the jack pad close-out, one of many areas on the ET where foam is applied at KSC. Voids 0.375 inch were detected in 1.75 inch thick foam using a pressure reduction of less than 0.4 psi. Of primary interest are areas of the ET that directly face the orbiter tile TPS. It is estimated that 90% of tile TPS damage on the orbiter 'belly' results from debonding SOFI during ascent. Test panels modeling these areas were manufactured with programmed debonds to determine the sensitivity of shearography as a function of debond size, SOFI thickness and vacuum. Results show repeatable detection of debonds with a diameter approximately half the SOFI thickness at less than 0.4 psi pressure reduction. Preliminary results are also presented on inspections of MSA-2 and the remote manipulator system (RMS) honeycomb material.

Davis, Christopher K.↗
NASA NTRS1995

Impact damage resistance and residual property assessment of (0/+/-45/90)s SCS-6/Timetal 21S

The impact damage resistance and residual mechanical properties of (0/ +/- 45/90)s SCS-6/Timetal 21S composites were evaluated. Both quasi-static indentation and drop-weight impact tests were used to investigate the impact behavior at two nominal energy levels (5.5 and 8.4 J) and determine the onset of internal damage. Through x-ray inspection, the extent of internal damage was characterized non-destructively. The composite strength and constant amplitude fatigue response were evaluated to assess the effects of the sustained damage. Scanning electron microscopy was used to characterize internal damage from impact in comparison to damage that occurs during mechanical loading alone. The effect of stacking sequence was examined by using specimens with the long dimension of the specimen both parallel (longitudinal) and perpendicular (transverse) to the 0 deg fiber direction. Damage in the form of longitudinal and transverse cracking occurred in all longitudinal specimens tested at energies greater than 6.3 J. Similar results occurred in the transverse specimens tested above 5.4 J. Initial load drop, characteristic of the onset of damage, occurred on average at 6.3 J in longitudinal specimens and at 5.0 J in transverse specimens. X-ray analysis showed broken fibers in the impacted region in specimens tested at the higher impact energies. At low impact energies, visible matrix cracking may occur, but broken fibers may not. Matrix cracking was noted along fiber swims and it appeared to depend on the surface quality of composite. At low impact energies, little damage has been incurred by the composite and the residual strength and residual life is not greatly reduced as compared to an undamaged composite. At higher impact energies, more damage occurred and a greater effect of the impact damage was observed.

Miller, Jennifer L.↗
NASA NTRS1996

Non-destructive evaluation of composites

The composite materials have been used in aerospace industries for quite some time. Several non-destructive evaluation (NDE) methods have been developed to inspect composites in order to detect flaws, matrix cracking, and delamination. These methods include ultrasonics, acoustic emission, shearography, thermography, X-ray, and digital image correlation. The NDE Branch of Marshall Space Flight Center has recently acquired a thermal imaging NDE system. The same system has been used at NASA Langley Research Center for detecting disbonds. In order to compare different NDE methods, three carbon/carbon composite panels were used for experiment using ultrasonic C-scan, shearography, and thermography methods. These panels have teflon inserts to simulate the delamination between plies in a composite panel. All three methods have successfully located the insert. The experiment and results are presented in the following sections.

Chu, Tsuchin Philip↗
NASA NTRS1998

Health Monitoring of Composite Material Structures Using a Vibrometry Technique

Non-destructive evaluation (NDE) methods for quantifying and locating damage are essential for inspecting structures to ensure safety and reliability. Transmittance function monitoring is a potentially new NDE technique being tested as a tool to detect, quantify, and locate damage on flexible structures. The technique has a large spatial range that is practical for detecting damage on large composite material structures such as a reusable launch vehicle. The Transmittance Function (TF) theory is based on structural dynamics principles that define how vibration at one point in a structure is related to a force at another point. This relationship is called the Frequency Response Function (FRF). A Transmittance Function (TF) is derived as the ratio of FRFs, and can detect damage because the FRFs change due to damage. If one excitation is used for the testing, the force does not need to be measured to compute the TF. In the damage detection procedure, the structure is subjected to wide-band vibration and TFs are computed between different accelerometers to detect changes in the structure, presumably due to damage. In the first year of the project the TF method was tested on a bolted panel, a curved panel, and beams, all made of fiberglass. It was shown that damage could be detected using low frequency vibration, 250 to 1,250 Hz. The technique is sensitive to damage, but it requires storage of historical or pre-damage TFs for the healthy structure. This would become a large data storage requirement for large structures. Thus one objective for the second year of the project was to eliminate the need to store historical data. The second year report gives details of how storage of historical data was eliminated. Further results of testing panel structures are also given.

Schulz, Mark J.↗
NASA NTRS1990

Non-Destructive Testing Scanner

Bio-Imaging Research's technology that originated in an aerospace program has come full circle with a new aerospace adaptation called the Advanced Computed Tomography Inspection System, or ACTIS. The medical version of CT scans the human body for tumors or other abnormalities, the ACTIS system finds imperfections in aerospace structures and components, such as castings, assemblies, rocket motors and nozzles. ACTIS is described by its developer as the most versatile CT scanner available for non-destructive testing applications. ACTIS is a variable geometry system. ACTIS source and detectors can be moved closer together or farther apart to optimize the geometry for different sizes of test objects. The combination of variable geometry, three sources, and focusing detectors makes ACTIS cost effective for a broad range of applications. System can scan anything from very small turbine blades to large rocket assemblies.

Source record↗
NASA NTRS1998

A New Approach to Overcoming Spatial Aliasing in Structural Damage Detection

Aircraft, reusable launch vehicles,unmanned aircraft, and other advanced structures are being built using lightweight composite materials/metals with design safety factors as low as 1.25. These advanced structures operate in uncertain and severe environments and are susceptible to damage such as delamination, fiber/matrix damage, hydrothermal strain in composite materials, and fatigue and cracking in metals. To ensure human safety and load-bearing integrity these structures must be inspected to detect and locate often invisible damage and faults before they become catastrophic. Conventional methods of non-destructive evaluation sometimes miss significant damage and are time consuming and expensive to perform. In contrast, vibrometry or vibration signature techniques are a global method of structural integrity monitoring that potentially can efficiently detect damage on large structures, including damage that is away from sensor locations, and in the interior of structures. However, a barrier problem in damage detection using vibration measurements is the need to measure the vibration response at a large number of points on the structure. Typically, model reduction or expansion procedures such as Guyan reduction or dynamic expansion are attempted to overcome the problem of insufficient measurements or spatial aliasing. These approaches depend on using information from the healthy model, and thus put error into the reduction used to represent the damaged structure. In this paper, a Frequency Response Function technique is used to detect damage to a fixed-free beam. The technique uses measured frequency response functions from the healthy structure as reference data, and then monitors vibration measurements during the life of the structure to detect damage. In an analytical simulation using a finite-element model of a beam, damage was located using only sparse measurements because the technique uses both rotation and translation measurements from the damaged structure.

Schulz, Mark J.↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
NASA NTRS1996

Finite Element Modeling of the Thermographic Inspection for Composite Materials

The performance of composite materials is dependent on the constituent materials selected, material structural geometry, and the fabrication process. Flaws can form in composite materials as a result of the fabrication process, handling in the manufacturing environment, and exposure in the service environment to anomalous activity. Often these flaws show no indication on the surface of the material while having the potential of substantially degrading the integrity of the composite structure. For this reason it is important to have available inspection techniques that can reliably detect sub-surface defects such as inter-ply disbonds, inter-ply cracks, porosity, and density changes caused by variations in fiber volume content. Many non-destructive evaluation techniques (NDE) are capable of detecting sub-surface flaws in composite materials. These include shearography, video image correlation, ultrasonic, acoustic emissions, and X-ray. The difficulty with most of these techniques is that they are time consuming and often difficult to apply to full scale structures. An NDE technique that appears to have the capability to quickly and easily detect flaws in composite structure is thermography. This technique uses heat to detect flaws. Heat is applied to the surface of a structure with the use of a heat lamp or heat gun. A thermographic camera is then pointed at the surface and records the surface temperature as the composite structure cools. Flaws in the material will cause the thermal-mechanical material response to change. Thus, the surface over an area where a flaw is present will cool differently than regions where flaws do not exist. This paper discusses the effort made to thermo-mechanically model the thermography process. First the material properties and physical parameters used in the model will be explained. This will be followed by a detailed discussion of the finite element model used. Finally, the result of the model will be summarized along with recommendations for future work.

Bucinell, Ronald B.↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Characteristics of Ferromagnetic Flux Focusing Lens in the Development of Surface/Subsurface Flaw Detector

Electromagnetic NDE techniques have in the past steered away from the use of ferromagnetic materials. Although their high permeabilities lead to increased field levels, the properties of ferrous elements in the presence of alternating magnetic fields are difficult to determine. In addition, their use leads to losses which can be minimized through the use of low conductivity ferrites. In fact, the eddy current probes which do incorporate ferromagnetic materials have focused on these losses and the shielding which can be obtained by surrounding a probe with a high permeability, conducting material. Eddy current probes enclosed in conducting and magnetic shields have been used to prevent the generated fields from interacting with materials in the vicinity of the probe, such as when testing near material boundaries. A recent invention has used ferromagnetic shielding to magnetically separate individual concentric eddy current probes in order to eliminate cross-talk between the probes so that simultaneous detection of different types of flaws at different depths can be achieved. In contrast to the previous uses of ferromagnetic materials purely as magnetic shields, an electromagnetic flaw detector recently developed at NASA Langley Research Center takes advantage of the flux focusing properties of a ferromagnetic mild steel in order to produce a simple, effective device for the non-destructive evaluation of conducting materials. The Flux Focusing Eddy Current Probe has been shown to accurately measure material thickness and fatigue damage. The straight forward flaw response of the probe makes the device ideal for rapid inspection of large structures, and has lead to its incorporation in a computer controlled search routine to locate fatigue crack tips and monitor experimental fatigue crack growth experiments.

Wincheski, Buzz↗
NASA NTRS1993

Industrial Inspection System

ACTIS+ is a smaller, personal computer version of ACTIS, a computed tomography system which finds its origins in the Apollo lunar landing research. It is used to identify imperfections in aerospace structures and components. Designed to be added to an existing real time radiography system, its components include a high precision rotation/elevation manipulator, color image monitor, graphical user interface monitor, and PC compatible workstation. Cross-sectional CT images are more detailed than radiographic images and the high speed scanning feature offers the capability for 100 percent inspection in a production environment. Bio-Imaging Research, Inc. (BIR) originally developed ACTIS for Marshall Space Flight Center (MSFC), and commercial units were sold to aerospace manufacturers. The technology was refined for ACTIS+, which is principally used in non-destructive testing.

Source record↗
NASA NTRS1991

Current and future developments in civil aircraft non-destructive evaluation from an operator's point of view

In June, 1988, the first International Conference on aging aircraft was held to address nondestructive tests (NDT) of aging aircraft and other issues. From this meeting, a research program was initiated and funded by the FAA. As a result of this program, a lot of work has been done to study current NDT practices in the aviation industry and secondly, to research and develop new NDT methods to improve the reliability and efficiency of in-service inspection of aircraft structures and powerplants. The following is an overview of the current and future developments in civil aircraft NDT, as viewed by an air carrier and the concerns for NDT in the future.

Aging Aircraft↗
NASA NTRS1994

Modal Test Technology as Non-Destructive Evaluation of Space Shuttle Structures

Modal test and analysis Is being used for nondestructive evaluation of Space Shuttle structures. The purpose of modal testing is to measure the dynamic characteristics of a structure to extract its resonance frequencies, damping, and mode shapes. These characteristics are later compared to subsequently acquired characteristics. Changes in the modal characteristics indicate damage in the structure. Use of modal test technology as a damage detection tool was developed at JSC during the Shuttle acoustic certification program and subsequent test programs. The Shuttle Modal Inspection System was created in order to inspect areas that are impossible or impractical to inspect with conventional methods. Areas on which this technique has been applied include control surfaces, which are covered with thermal protection tiles, and the Forward Reaction Control Module, which is a frame structure that supports various tanks, thrusters, and fluid lines, which requires major disassembly to inspect. This paper traces the development of the technology, gives a status of its implementation on the Shuttle, explains challenges involved in implementing this type of inspection program, and suggests future improvements in data analysis and interpretation. Dual-use applications of the technology include inspections of bridges, oil-platforms, and aircraft.

Micheal S Grygler↗
DOE OSTI2025

Monte Carlo N-Particle Transport Performance of Predicting Digital Radiographic IQI Inspection

The identification of porosity, geometric noncompliance, and other defect types are critical to the qualification of materials and components. X-ray radiographic nondestructive testing is a common industrial inspection method for process quality control and component qualification and certification. Digital radiography provides a quick and efficient alternative when compared to traditional film-based inspection. The quality of radiographic inspection is dependent on equipment specifications, such as the source spot size and detector pixel size, and the specific parameters selected for use for the radiographic technique. To evaluate if an x-ray system and technique is sufficient for a given requirement, a radiographic image quality indicator (IQI) can be used. Radiographic IQIs in hard to machine materials or hard to manufacture defects can be time consuming and expensive to manufacture. This study was conducted to evaluate current Savannah River National Laboratory (SRNL) x-ray imaging systems with a custom tantalum IQI and using Monte Carlo simulations to predict the performance of future systems. The tantalum IQI was tested using a Siefert Isovolt 420 keV x-ray tube with a Perkin Elmer XRD 1611 flat panel with 100-micron pixels. Using the Monte Carlo N-Particle transport software, the radiographic tally was used to simulate the photon flux through an identical tantalum IQI. These simulations provided a benchmark as to the best theoretical identification on a given system using our tantalum IQI. The simulations were refined to match SRNL’s current systems’ noise levels, leading to confidence in their ability to predict the performance of other systems that may be purchased and deployed in the future at the Savannah River Site. Future studies will be conducted to prove this research can be extended to artificially evaluate the ability for systems to identify critical defect sizes through x-ray radiographic inspection, drastically reducing the cost and time burdens of producing high-fidelity radiographic test articles.

digital X-ray radiography↗
DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗
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)↗
DOE OSTI2026

Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578

Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.

Lorier, Troy [Savannah River National Laboratory (↗