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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment and Usage of In-Situ Monitoring Data for American Society of Mechanical Engineers Part Qualification

In-situ monitoring and anomaly detection are important components for qualification of directed energy deposition (DED) additive manufacturing (AM) processes and components. The use of in-situ monitoring requires an understanding of anomalies that can be identified during the process and how those anomalies correlate to mechanical properties of the component post-production. There is also a need to qualify the algorithms and software used to interpret the process signals for DED AM. There is no single process signal that can be used with a single algorithm that will identify all anomalies that will translate to a defect in a process. The process signals are affected by changes in material, location, resolution, acquisition rate, component geometry, and the machine itself. It is observed that multiple process signals are required to identify relevant features that can be correlated to mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on statistical processes or feedstock control, a sequence of machine learning models was trained on 6299 tensile specimens to locally predict the tensile properties of stainless-steel parts based on fused multi-modal in situ sensor data and a priori information. A cyberphysical infrastructure enabled the robust spatial tracking of individual specimens, and computer vision techniques registered the ground truth tensile measurements to the in situ data. The co-registered 230 GB dataset used in this work has been publicly released and is available as a set of HDF5 files. The extensive training data requirements and wide range of size scales were addressed by combining deep learning, machine learning, and feature engineering algorithms in a relay. The trained models demonstrated a 61% error reduction in ultimate tensile strength predictions relative to estimates made without any in situ information. Lessons learned and potential improvements to the sensors and mechanical testing procedure are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

We introduce SymbolFit (API: https://github.com/hftsoi/symbolfit), a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods. We demonstrate the framework in high-energy physics experiments at the CERN Large Hadron Collider (LHC) using five real proton-proton collision datasets from new physics searches, including background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We show that our framework can flexibly and efficiently generate a wide range of candidate functions that fit a nontrivial distribution well using a simple fit configuration that varies only by random seed, and that the same fit configuration, which defines a vast function space, can also be applied to distributions of different shapes, whereas achieving a comparable result with traditional methods would have required extensive manual effort.

Tsoi, Ho Fung [Univ. of Pennsylvania, Philadelphia↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Large space structures fabrication experiment

The fabrication machine used for the rolltrusion and on-orbit forming of graphite thermoplastic (CTP) strip material into structural sections is described. The basic process was analytically developed parallel with, and integrated into the conceptual design of, a flight experiment machine for producing a continuous triangular cross section truss. The machine and its associated ancillary equipment are mounted on a Space Lab pallet. Power, thermal control, and instrumentation connections are made during ground installation. Observation, monitoring, caution and warning, and control panels and displays are installed at the payload specialist station in the orbiter. The machine is primed before flight by initiation of beam forming, to include attachment of the first set of cross members and anchoring of the diagonal cords. Control of the experiment will be from the orbiter mission specialist station. Normal operation is by automatic processing control software. Machine operating data are displayed and recorded on the ground. Data is processed and formatted to show progress of the major experiment parameters including stable operation, physical symmetry, joint integrity, and structural properties.

Source record↗
NASA NTRS1996

Investigation of Machine Design for Friction Stir Welding

The process of joining two pieces of metal together has not significantly changed over the last few decades. The basic idea used is to bring the pieces together and apply enough heat to melt the metal at the interface. The molten metal mixes and after cooling forms a strong joint. This process is called the fusion process. The most significant difference between the many fusion processes is how the heat is generated and applied. The Welding Institute (TWI), in Great Britain, has recently patented an innovative application of mechanical friction. TWI designed a tool and process called Friction Stir Welding (FSW) that uses friction to heat the metal to within a few hundred degrees Fahrenheit of melting, just to the point of being plastic-like. The tool then stirs the plasticized metal together forming a joint that has been shown to be as good or better than an equivalent fusion joint. The FSW process is well suited for the joining of the aluminum alloys used in the aerospace industry. The relatively low melting point of aluminum eliminates the requirements for exotic materials for pin tool design. The FSW process has been successfully used to join alloys such as 7075 which were before considered "unweldable", and aluminum-lithium 2195 which exhibits many problems when fusion welded. The objective this summer was to investigate the design of a FSW system that could take this process from the laboratory to the manufacturing floor. In particular, it was the goal of my NASA colleague to develop a concept for applying the FSW process to the manufacturing of aluminum cryogenic oxygen and hydrogen tanks, of the sort used to make the Shuttle External Tank.

Romine, Peter L.↗
DOE OSTI2026

Time-resolved spray characterization via unified optical flow and binarization technique

This work leverages an unsupervised machine learning and advanced image processing techniques to characterize the breakup of fuel sprays in a small-scale combustor under reacting conditions, providing valuable insights into near-nozzle flow phenomenology. The proposed methodology integrates an improved optical flow model on a convolutional neural network to extract flow vectors with a binarization technique to assess droplets’ size and shape across the region of interest. The velocimetry approach demonstrates superior performance compared to a state-of-the-art optical flow model when applied to high-speed X-ray phase contrast spray images, achieving more accurate and reliable flow predictions. Moreover, breakup processes are quantified by breakup length and sphericity in accordance with velocity estimations, allowing a more complete characterization of the flow. This study establishes a robust methodology for analyzing spray morphology and primary breakup in compact combustors, contributing valuable means of understanding and optimizing fuel spray behavior in advanced combustion systems.

42 ENGINEERING↗
DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling

The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.

Digital twins↗
DOE OSTI2026

Constraining microphysical processes of warm rain formulation using advanced spectral separations, an ensemble retrieval framework and machine learning techniques

Drizzle, a common feature of marine boundary layer clouds formed through collision coalescence, plays a key role in cloud microphysics and evolution. Yet, simultaneously retrieving cloud and drizzle properties from remote-sensing observations remains challenging because drizzle droplets often dominate radar signals, masking cloud contributions. The goal of the proposed research is to provide constraints for the process of autoconversion and accretion using ARM cloud measurements. Specifically, we provide concurrent retrievals of cloud and drizzle that allows users to derive corresponding autoconversion and accretion rates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Fabrication of Polyimide-Matrix/Carbon and Boron-Fiber Tape

The term HYCARB denotes a hybrid composite of polyimide matrices reinforced with carbon and boron fibers. HYCARB and an improved process for fabricating dry HYCARB tapes have been invented in a continuing effort to develop lightweight, strong composite materials for aerospace vehicles. Like other composite tapes in this line of development, HYCARB tapes are intended to be used to build up laminated structures having possibly complex shapes by means of automated tow placement (ATP) - a process in which a computer-controlled multiaxis machine lays down prepreg tape or tows. The special significance of the present process for making dry HYCARB for ATP is that it contributes to the reduction of the overall cost of manufacturing boron-reinforced composite-material structures while making it possible to realize increased compression strengths. The present process for making HYCARB tapes incorporates a "wet to dry" process developed previously at Langley Research Center. In the "wet to dry" process, a flattened bundle of carbon fiber tows, pulled along a continuous production line between pairs of rollers, is impregnated with a solution of a poly(amide acid) in N-methyl-2-pyrrolidinone (NMP), then most of the NMP is removed by evaporation in hot air. In the present case, the polyamide acid is, more specifically, that of LaRC. IAX (or equivalent) thermoplastic polyimide, and the fibers are, more specifically, Manganite IM7 (or equivalent) polyacrylonitrile- based carbon filaments that have a diameter of 5.2 m and are supplied in 12,000-filament tows. The present process stands in contrast to a prior process in which HYCARB tape was made by pressing boron fibers into the face of a wet carbon-fiber/ poly(amide acid) prepreg tape . that is, a prepreg tape from which the NMP solvent had not been removed. In the present process, one or more layer(s) of side-by-side boron fibers are pressed between dry prepreg tapes that have been prepared by the aforementioned gwet to dry h process. The multilayer tape is then heated to imidize the matrix material and remove most of the remaining solvent, and is pressed to consolidate the multiple layers into a dense tape. For tests, specimens of HYCARB tapes and laminated composite panels made from HYCARB tape were prepared as follows: HYCARB tapes were fabricated as described above. Each panel was made by laying down ten layers of tape, containing, variously, one, two, or three boron-fiber plies and the remainder carbon- fiber-only plies (see figure). Each panel was made by laying down ten layers of tape. Each panel was then cured by heating to a temperature of 225 C for 15 minutes, then pressing at 200 psi (A1.4 MPa) while heating to 371 C, holding at 371 C for 1 hour, then continuing to hold pressure during cooling. Control specimens that were otherwise identical except that they did not contain boron fibers also were prepared. In room-temperature flexural tests, the HYCARB specimens performed comparably to the control specimens; in room-temperature, open-hole compression tests, the HYCARB specimens performed slightly better, by amounts that increased with boron content.

Belvin, Harry L.↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1966

Sculpturing, forming, and spinning of large components for launch vehicles

At .the beginning of the S-IC booster contract, it was evident that certain manufacturing techniques would have to be developed before the proposed design could be put into full production. Much of the development activity that has been accomplished was initiated because the size of booster components was greater than the capacities of available equipment. This paper describes some of the more significant methods that have evolved from this development activity, and which demonstrate how the capability to manufacture S-IC components such as bulkhead gore segments, tank skins, Y-rings, and lox tunnels, has been acquired. The fabrication methods used to transform 2219 aluminum alloy plate into the large, sculptured, and contoured parts that make up the structural surfaces of the booster include hydraulic bulge forming, age forming, and adaptations of conventional skin milling and chemical milling processes. The importance of fixturing and the machining sequence related to fabrication of the Y-ring are pointed out. The shear-spinning process that is used to produce a seamless, one-piece lox tunnel 40 feet long from a cylindrical blank only 62 inches long is described. The presentation is concluded with a synopsis of the current design changes that are being implemented to improve strength-to-weight ratios. A brief statement of possible future activity is included.

Milling↗
NASA NTRS2002

Surface Enhancement Improves Crack Resistance

The low plasticity burnishing (LPB) process produces a deep layer of surface compression in a quick and affordable manner to produce metal surfaces free of scratches, nicks, and gouges. The process, designed for easy inclusion in the manufacturing environment, can be performed with conventional Computer Numerical Control machine tools. This allows parts to be processed during manufacturing, rather than as a post process in a separate facility. A smooth, free-rolling spherical ball suspended in a fluid allows for single-point contact. The ball comes into mechanical contact only with the surface to be burnished, and can be moved in any direction. LPB can be applied to all types of carbon and alloy steel, stainless steel, cast iron, aluminum, titanium, and nickel- based super alloys. In addition to improving a surface's resistance to fatigue and damage, treatment stops the growth of shallow cracks. The LPB process is used on the leading edges of turbine blades to improve resistance to foreign object damage and crack growth. This means significant savings for aircraft owners, since maintenance requirements to inspect for fatigue damage, replace parts, and remove corrosion damage increase the cost of operation.

Source record↗
DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2024

High plasticity in refractory composite fabrication by ultrasonic additive manufacturing

Refractory metal composites are desirable for use in extreme environments that require materials with high specific strengths and resilience to external environments such as that found in nuclear and aerospace. However, due to the high melting temperature of refractories, liquid state joining processes such as welding remains difficult. Ultrasonic additive manufacturing (UAM) provides a potential route for processing refractory composites because it is a solid-state (i.e., no-melting) process and allows for intermittent machining operations to be performed between welds. Further, to demonstrate refractory composite fabrication, this study utilized UAM to machine a cavity to locate and sequester a Mo foil in a Zircaloy-4 (Zry-4) baseplate and additively build over the top with Zry-4 foils, thereby embedding the Mo in Zry-4 matrix. Significant deformation of the Zry-4 microstructure was observed: this deformation caused adiabatic heating and subsequent dynamic recrystallization through the transformation from α→β and then back to α as the material cooled. Flexural testing of the Zr–Mo composite revealed no delamination or failure, but the strength was not as expected, falling lower than a cold-worked Zry-4 sample. Finite element analysis supported that some bonding must have existed between the Zry-4 and Mo. There was indeed an interdiffusion zone at the Zry-4 foil–Mo foil interface, observing a metastable body-centered cubic β-Zr lathe. It was determined that sufficient strain energy was present to encourage the nucleation of the β-Zr grain along α-Zr grains. Future work is warranted to investigate UAM for refractory composite fabrication.

36 MATERIALS SCIENCE↗