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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A semiempirical transparency model for dual energy cargo radiography applications

Cargo containers passing through ports are scanned by non-intrusive inspection systems to search for concealed illicit materials. By using two photon beams with different energy spectra, dual energy inspection systems are sensitive to both the area density and the atomic number of cargo contents. Most literature on the subject assumes a simple exponential attenuation model for photon intensity in which only free streaming photons are detected. However, this approximation neglects second order effects such as scattering, leading to a biased model and thus incorrect material predictions. This work studies the accuracy of the free streaming model by comparing it to simulation outputs, finding that the model shows poor atomic number reconstruction accuracy at high- Z and suffers significantly if the source energy spectra and detector response function are not known exactly. To address these challenges, this work introduces a semiempirical transparency model which modifies the free streaming model by rescaling different components of the mass attenuation coefficient, allowing the model to capture secondary effects ignored by the free streaming model. The semiempirical model displays improvement agreement with simulated results at high- Z and shows excellent extrapolation to materials and thicknesses which were not included during the calibration step. Furthermore, this work demonstrates that the semiempirical model yields accurate atomic number predictions even when the source spectra and detector response are not known exactly. In conclusion, using the semiempirical model, manufacturers can perform a simple calibration to enable more precise Z reconstruction capabilities, which has the potential to significantly improve the performance of existing dual energy radiographic systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗
DOE OSTI2020

A Systematic Evidence Based Performance Approach to Regulation of Nuclear Sites in England and Wales - 20058

The Environment Agency for England has developed a systematic evidence-based approach to pursue our strategic environmental objectives for the regulation of nuclear sites. We use an annual evidence review process to ensure effective and efficient targeting of limited resources to achieve those objectives. Over the last 8 years, we have been developing and refining this approach to ensure risk-based and value driven regulation. The approach comprises nuclear site and nuclear sector review processes known as Site Environment Review (SER) and Nuclear Environment Review (NER). This approach complements our regulation of nuclear site permit holders under the Environmental Permitting Regulations (EPR). We deliver our regulation of nuclear sites in England and Wales alongside the Office for Nuclear Regulation. The SER process involves the lead regulator for each nuclear site assessing the permit holder's environmental performance across 14 themes, set within the context of the site's main activities and associated waste disposals. Our themes include environmental leadership, resources and climate change, radioactive waste management, facility management and decommissioning, groundwater, and environmental radiological protection. We use evidence from site inspections and working within our subject matter groups to grade current and predicted future environmental performance. We are particularly interested in sustainability and the application of Best Available Techniques (BAT) to prevent the creation, and minimise the disposal, of radioactive wastes. We use risk analysis (strengths, weaknesses, threats and opportunities) to examine performance against our strategic environmental objectives, which are set out, in our 5-year Nuclear Delivery Plan (NDP). The output supports the targeting of our resources at each nuclear site. We consult the relevant permit holders on the SER priorities and use their feedback to refine our plans. We expect all permit holders to take account of our priorities when considering their own programmes of work, objectives and plans. The NER process brings together what we learn and achieve through regulation across the sector. It provides input to planning priorities, supported by qualitative and semi-quantitative evidence. It covers the 28 nuclear sites in England and Wales and spans the same 14 environmental themes. During the process we collate, compile and summarise evidence from the SERs and other sources such as inspection reports and evidence from our other nuclear work programmes. The output is the NER annual report. This provides a snapshot of the status of the nuclear sector and gives insights to enable us to regulate more efficiently and effectively. It also takes account of cross-cutting issues and risks such as changes in international standards, domestic policy, regulatory framework, domestic standards and guidance, learning from experience such as incidents, events and good practice, and innovation, research and development. It provides graphics that illustrate the grading of environmental performance for the nuclear sector across the fourteen environmental themes. This analysis allows benchmarking of nuclear site's environmental performance and the visualisation provides a convenient comparison of performance across themes, sites, and over time. We use this intelligence to inform our investment in training and development of our staff, and our cross-cutting engagement on strategic issues with government, the Nuclear Decommissioning Authority (NDA) and other corporate organisations. Adopting this approach can provide benefits with organisational reputation, stakeholder participation and ensuring value from the public investment. This paper describes the history of the SER/NER process, a selection of outputs from the process and ideas for improvement. The paper will be of interest to other regulators and organisations across the world that are interested in supporting continuous improvement. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Enhancing the ATR Primary Coolant System: A 3D Modeling Approach

The project consisted in system inspections to the ATR Primary Coolant System involving welds, fittings, motors, pumps, flanges, and heat exchangers to enhance Inservice Inspection program as required by DOE orders. NOTE: This article is to be published as "DOE & DOE Contractors Only" in the OPEXShare application, which means it will be available for viewing to DOE & DOE Contractor registered users only. This article can also be used by CAES for their training and safety meetings.

42 - ENGINEERING↗
DOE OSTI2023

A combination interferometric and morphological image processing approach to rapid quality assessment of additively manufactured cellular truss core components

Advanced manufacturing (AM) processes such as laser powder bed fusion (LPBF) are increasingly capable of fabricating components with useful and unprecedented mechanical properties by incorporating complex internal bracing structures. From the standpoint of quality control and assessment, however, internally complex assemblies present significant build-verification challenges. Here we propose a hybrid approach to the inspection involving the application of computer-aided speckle interferometry (CASI) and morphological image processing as a rapid, inexpensive, and facile method for AM quality control. The described methodology has low capital equipment costs, is full-field and non-contact, can be used in an industrial setting, and has very low requirements in terms of operator training and expertise. Consisting primarily of the combination of image processing software with a simple optical system of variable sensitivity, the method is shown to be effective for inspection of a titanium honeycomb component subjected to differential pressure. Results are compared to those achieved with computed tomography (CT), immersion ultrasound testing (UT), and optical holographic interferometry. Here we propose several possible processing strategies for automated quality assessment based on this powerful hybrid approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evolution of global and local deformation in additively manufactured octet truss lattice structures

Additively manufactured lattice truss structures, often referred to as architected cellular materials, present significant advantages over conventional structures due to their unique characteristics such as high strength-to-weight ratios and surface area-to-volume ratios. These geometrically complex structures, however, come with concomitant challenges for qualification and inspection. In this study, compression testing interrupted with micro-computed tomography inspection was conducted to monitor the evolution of global and local deformation throughout the loading process of 304L stainless steel octet truss lattice structures. Both two- and three-dimensional image analysis techniques were leveraged to characterize geometric heterogeneities resulting from the laser powder bed fusion manufacturing process as well as track the structure throughout deformation. Variations from model-predicted behavior resulting from these heterogeneities are considered relative to the predicted and actual responses of the structures during compression to better understand, model, and predict the octet truss lattice structure compression response.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detecting keyhole porosity and monitoring process signatures in additive manufacturing: an in situ pyrometry and ex situ X-ray radiography correlation

Creation of pores and defects during laser powder bed fusion (LPBF) can lead to poor mechanical properties and thus must be minimized. Post-build inspection is required to ensure the printed parts contain acceptably low defect concentrations. These inspections are time consuming and costly, especially for large or complex parts. As a potential solution, in situ process monitoring can be used to detect the creation of defects, characterize local material behavior and predict expected component properties. However, the precise relationship between pore creation and in situ process monitoring still needs to be understood. In this work, high-speed infrared diode-based pyrometry and high-speed optical imaging signals were used to monitor LPBF printing of 446 stainless steel 316 L single tracks with varying laser power and velocity. Results indicate an increase in pyrometer signal and melt pool dimensions with increasing laser power and decreasing velocity in agreement with previous work. In addition, careful analysis of pyrometer signal reveals a distinct signature of the conduction-to-keyhole mode transition which was confirmed by metallography. Critically, pore defect initiation as characterized by ex situ X-ray radiography was correlated with in situ thermal monitoring signals to derive the probability of defect creation. Our results show that, in principle, a probabilistic prediction of pore formation can be achieved based on in situ high-speed pyrometry monitoring of the LPBF melt pool.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2021

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↗
DOE OSTI2022

Demonstration of RELAP5-3D for transient analysis of a dual coolant lead lithium fusion blanket concept

Our research aims to build a multiphysics framework for transient analysis of the Dual- Coolant Lead-Lithium (DCLL) blanket design of the proposed Fusion Nuclear Science Facility (FNSF) for all Onticipated Operational Occurrences (AOOs) using Reduced Order Models (ROMs). This paper presents an assessment of using RELAP5-3D for transient thermal-hydraulic blanket analysis of the DCLL design. Preliminary validation studies for the RELAP5-3D properties were conducted using a representative vertical flow loop model and a comparison study using experimental data from the thermal convection corrosion loops at ORNL. These studies inspected the thermal hydraulic response of Lead-Lithium Eutectic (PbLi) systems within the RELAP5-3D code; mainly pressure drop and heat transfer, as compared to analytical calculations and experimental data. The Magnetohydrodynamic (MHD) pressure drop effect for liquid metal under the effects of a uniform magnetic field was implemented into our models using an equivalent forms loss coefficient based on the phasic momentum equations. We verified our implementation of MHD pressure drop within RELAP5-3D using a systematic approach consisting of two studies. Models were developed to perform comparison studies based on experimental data from the Argonne Liquid metal EXperiment (ALEX) facility and the Magnetohydrodynamic PbLi Experiment (MaPLE) facility. Using the developed validation basis, we built a simplified DCLL channel model within RELAP5-3D, inspected MHD pressure drop and performed transient temperature analysis. The DCLL channel model used radial heating data from the International Thermonuclear Experimental Reactor (ITER) which was normalized to the FNSF design. Here, a representative startup transient was performed using a relative power curve from the initial ramp up of a long-term power pulse of the Tore Supra reactor. This study demonstrated that RELAP5-3D ROMs are capable of accurate transient analysis of the DCLL blanket. We have since utilized MCNP to develop heating profiles based on the FNSF design and are working toward implementing them into our DCLL model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models

Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Confirming the absence of nuclear warheads via passive gamma-ray measurements

Arms-control agreements between the United States and Russia negotiated after the end of the Cold War have imposed limits on the number of deployed strategic nuclear weapons. Verification of these agreements has relied on onsite inspections, sometimes supported by radiation detection techniques to confirm the absence of a nuclear warhead when ambiguities arise. So far, these measurements have sought to detect neutron emissions associated with the presence of plutonium, but they would be inadequate for uranium devices. In an effort to offer instruments that could be used to confirm the absence of both plutonium and uranium weapons, here we propose an inspection system that uses only passive gamma radiation detection techniques. Such a system would be particularly valuable for next-generation arms-control agreements that limit total numbers of weapons and would involve containerized items in storage. We conducted extensive Monte Carlo simulations to support the development of a verification protocol and detection algorithm. Here, we demonstrate the viability of the technique using standard laboratory check sources and MCNP simulations for simplified configurations of special nuclear material.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Fast-neutron/gamma-ray radiography using a broad-energy neutron source

Transmission radiography is a well-established nondestructive examination technique with widespread application to fields ranging from medicine to security. Traditionally, inspection is performed using a single particle type (such as x-rays). However, the information available using a single probe for traditional radiography is limited. The present work evaluates material discrimination via transmission radiography using the attenuation ratio of monoenergetic gamma rays to a broad spectrum of fast neutrons. Here, the method was assessed using an 241 Am/Be radioisotope source that provides both 4.4 MeV gamma rays and a spectrum of fast neutrons up to 12 MeV. A total of 14 object configurations were measured: seven different materials each with two thicknesses (2.5 cm and 5 cm). The ability to distinguish materials was evaluated and shows more significant variation among atomic numbers than for high-energy x-rays alone, making it easier to distinguish between classes such as low-, mid-, and high-Z materials. These results suggest that superior material discrimination is also possible using a combination of monoenergetic gamma rays and broad-spectrum fast neutrons from a variety of nuclear reactions, such as 11 B(d,nγ) 12 C, that could be implemented in future inspection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Improving operational performance using machine learning analysis of Radiation Portal Monitor measurements

Radiation Portal Monitors (RPMs) have been installed worldwide to scan vehicles and cargo for the presence of radiological and nuclear materials. In field operations, the sensitivity of these systems is typically limited by the relatively high rates of nuisance alarms that usually must be followed up with secondary inspections. We have developed a machine-learning based alarm analysis system that has been deployed at numerous locations in the U.S. and internationally. Our Enhanced Radiological Nuclear Inspection and Evaluation (ERNIE) analysis software and its derivatives have demonstrated increased sensitivity to radiological and nuclear material of concern while reducing nuisance alarms by as much as an order of magnitude.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗