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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗
NASA NTRS1998

Determining Fault Insertion Rates for Evolving Software Systems

In developing a software system, we would like to be able to estimate the way in which the fault content changes during its development, as well as determining the locations, having the highest concentration of faults.

faults incremental code enhancements↗
NASA NTRS2006

Catastrophic Fault Recovery with Self-Reconfigurable Chips

Mission critical systems typically employ multi-string redundancy to cope with possible hardware failure. Such systems are only as fault tolerant as there are many redundant strings. Once a particular critical component exhausts its redundant spares, the multi-string architecture cannot tolerate any further hardware failure. This paper aims at addressing such catastrophic faults through the use of 'Self-Reconfigurable Chips' as a last resort effort to 'repair' a faulty critical component.

fault tolerance↗
NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗
DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Cohesion-Induced Stabilization in Stick-Slip Dynamics of Weakly Wet, Sheared Granular Fault Gouge

We use three-dimensional discrete element calculations to study stick-slip dynamics in a weakly wet granular layer designed to simulate fault gouge. The granular gouge is constituted by 8000 spherical particles with a poly-disperse size distribution. At very low liquid content, liquids impose cohesive and viscous forces on particles. Our simulations show that by increasing the liquid content, friction increases and granular layer shows higher recurrence time between slip events. We also observe that slip events exhibit larger friction drop and layer compaction in wet system compared to dry. We demonstrate that a small volume of liquid induces cohesive forces between wet particles that are responsible for an increase in coordination number leading to a more stable arrangement of particles. This stabilization is evidenced with two orders of magnitude lower particle kinetic energy in wet system during stick phase. Similar to previous experimental studies, we observe enhanced frictional strength for wet granular layers. In experiments, the physicochemical processes are believed to be the main reason for such behavior, we show however, that at low confining stresses the hydromechanical effects of induced cohesion are sufficient for observed behavior. Our simulations illuminate the role of particle interactions and demonstrate the conditions under which induced cohesion plays a significant role in fault zone processes, including slip initiation, weakening, and failure.

58 GEOSCIENCES↗
DOE OSTI2018

EReinit: Scalable and efficient fault-tolerance for bulk-synchronous MPI applications

Scientists from many different fields have been developing Bulk-Synchronous MPI applications to simulate and study a wide variety of scientific phenomena. Since failure rates are expected to increase in larger-scale future HPC systems, providing efficient fault-tolerance mechanisms for this class of applications is paramount. The global-restart model has been proposed to decrease the time of failure recovery in Bulk-Synchronous applications by allowing a fast reinitialization of MPI. However, the current implementations of this model have several drawbacks: they lack efficiency; their scalability have not been shown; and they require the use of the MPI profiling interface, which precludes the use of tools. Here, we present EReinit, an implementation of the global-restart model that addresses these problems. Our key idea and optimization is the co-design of basic fault-tolerance mechanisms such as failure detection, notification, and recovery between MPI and the resource manager in contrast to current approaches on which these mechanisms are implemented in MPI only. We demonstrate EReinit in three HPC programs and show that it is up to four times more efficient than existing solutions at 4,096 processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A New Simplified Discrete Fracture Model for Shearing of Intersecting Fractures and Faults

Abstract Shearing of fractures and faults is important because it can result in permeability change or even induce seismicity—both are keys for efficient and safe energy recovery and storage in Earth systems. Quantitative analysis of shearing of intersecting fractures and faults is challenging because it can involve dynamic frictional contacts that are complicated by deformation of the rock matrix. To predict the shearing of intersecting fractures/faults, we attempt to answer the question of how intersections impact the shearing of a fracture network and whether we can simplify the description as compared to classical discrete fracture network (DFN) models. To answer these questions, we conducted a series of numerical simulations on scenarios for variable numbers of intersecting fractures. All these examples yield consistent results: the results of using DFNs are consistent with those of using hypothetical major paths. This leads to a new model, which we name simplified discrete fracture network model , to analyze shearing of intersecting fractures/faults using major path(s). We found that the intersections of fractures do not fundamentally change the shearing of two intersecting fractures if the intersecting angles are small. Furthermore, increasing the number of fractures/faults may relax the stress as more fractures/faults become available for shearing and distributing the stress. The simplified DFN model, which can capture efficiently the shearing behavior of each major paths from a large number of intersecting fractures/faults, will be a promising conceptual model that is complementary to existing equivalent continuum and discrete fracture models to analyze shearing of intersecting fractures/faults.

58 GEOSCIENCES↗
DOE OSTI2017

RedThreads: An Interface for Application-Level Fault Detection/Correction Through Adaptive Redundant Multithreading

In the presence of accelerated fault rates, which are projected to be the norm on future exascale systems, it will become increasingly difficult for high-performance computing (HPC) applications to accomplish useful computation. Due to the fault-oblivious nature of current HPC programming paradigms and execution environments, HPC applications are insufficiently equipped to deal with errors. We believe that HPC applications should be enabled with capabilities to actively search for and correct errors in their computations. The redundant multithreading (RMT) approach offers lightweight replicated execution streams of program instructions within the context of a single application process. Furthermore, the use of complete redundancy incurs significant overhead to the application performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. Finally, it showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A performance evaluation framework for building fault detection and diagnosis algorithms

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more effort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and effectiveness of both research-grade FDD algorithms and commercial products - a state of affairs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A robust framework for frictional fault contact in geological formations using a stabilized augmented Lagrangian approach

Numerical simulations are essential to evaluate the performance and safety of engineered subsurface systems such as geological carbon storage sites, enhanced geothermal fields, and oil and gas reservoirs. A key challenge lies in accurately modeling the frictional contact behavior along fault surfaces. This problem involves inequality constraints that arise from the physics of frictional slip, requiring specialized numerical methods to handle the resulting highly nonlinear and path-dependent behavior. Here, in this work, we address this challenge using an Augmented Lagrangian Method (ALM) implemented via the Uzawa algorithm. The formulation employs mixed finite element spaces, combining low-order piecewise linear displacements within the 3D domain cells with piecewise constant tractions defined on the fault surfaces. Furthermore, to ensure stability and satisfy the inf-sup condition, the discrete displacement space is enriched with face bubble functions on both sides of the contact interfaces. This approach offers several advantages over other stabilization techniques that rely on additional terms, and it integrates naturally in the Uzawa framework.

58 GEOSCIENCES↗
DOE OSTI2022

Formation of I 1 stacking fault by deformation defect evolution from grain boundaries in Mg

I 1 stacking faults (SFs) in Mg alloys are regarded as the nucleation sites of $\langle \text{c+a} \rangle$ dislocations that are critical for these alloys to achieve high ductility. Previously it was proposed that the formation of I 1 SFs requires the accumulations of a large number of vacancies, which are difficult to achieve at low temperatures. In this study, molecular dynamics (MD) and molecular statics (MS) simulations based on empirical interatomic potentials were applied to investigate the deformation defect evolutions from the symmetric tilt grain boundaries (GBs) in Mg and Mg-Y alloys under external loading along $\langle c \rangle$-axis. The results show the planar faults (PFs) on Pyramidal I planes first appear due to the nucleation and glide of $\langle {\frac {1}{2}}c + p\rangle$ partial dislocations from GBs, where $\langle p\rangle$ = ${\frac {1}{3}} \langle10\bar{1}0\rangle$. These partial dislocations with pyramidal PFs interact with other defects, including pyramidal PFs themselves, GBs, and $\langle p\rangle$ partial dislocations, generating a large amount of I 1 SFs. Detailed analyses show the nucleation and growth of I 1 SFs are achieved by atomic shuffle events and deformation defect reactions without the requirements of vacancy diffusion. Our simulations also suggest the Y clusters at GBs can reduce the critical stress for the formation of pyramidal PFs and I 1 SFs, which provide a possible reason for the experimental observations that Y promotes the $\langle{\text{c+a}}\rangle$ dislocation activities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The impact of nickel concentration and stacking fault energy on deformation mechanisms in high-purity austenitic Fe-Cr-Ni alloys

Understanding how composition affects deformation mechanisms in austenitic stainless steels is essential for developing accurate predictive models of stress-induced failures and stress corrosion cracking. Nickel (Ni), an element classified as a critical element, plays a crucial role in these processes. It is important to examine how Ni concentration influences stacking fault energy (SFE) and, consequently, the deformation mechanisms of austenitic stainless steels. However, in commercial stainless steels, the effects of other alloying elements and impurities can obscure Ni's role, complicating efforts to isolate its impact. Here, in this study, we use two high-purity Fe-Cr-Ni alloys to investigate how Ni concentration and SFE interact to alter deformation mechanisms and induce martensitic transformation. By combining in situ synchrotron X-ray diffraction (XRD) tensile testing and post-mortem electron microscopy with density functional theory simulations, we gain precise insights into these phenomena. We find that the Fe18Cr10Ni (wt%) alloy, with its low SFE, exhibits higher stacking fault probability, deformation-induced martensitic transformation, and a lesser increase in dislocation density with plastic strain. In contrast, the Fe18Cr14Ni (wt%) alloy, with its higher SFE, shows enhanced deformation twinning and greater dislocation density with increasing strain. These findings from high-purity ternary alloys provide valuable insights that can guide the search for alternative elements to replace Ni while achieving similar effects on phase stability and deformation behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is often unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.

17 WIND ENERGY↗
DOE OSTI2019

Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

58 GEOSCIENCES↗
DOE OSTI2021

Methane Plume Emissions Associated With Puget Sound Faults in the Cascadia Forearc

Methane gas plumes have been discovered to issue from the seafloor in the Puget Sound estuary. These gas emission sites are co-located over traces of three major fault zones that fracture the entire forearc crust of the Cascadia Subduction Zone. Multibeam and single-beam sonar data from cruises conducted in 2011, 2018, 2019, 2020 and 2021 identified the acoustic signature of over 330 bubble plumes. Dissolved gas from the plumes combines to elevate seawater methane concentrations of the entire Puget Sound estuary. Fluid samples from adjacent terrestrial hot springs and deep-water wells surrounding the estuary contain a helium-3 isotope signature, indicating a deep fluid source located near the underlying Cascadia Subduction Zone. However, Puget Sound seawater emission sites lack chemical isotope signatures and elevated thermal anomalies expected from association with a deep plate-interface reservoir. The existence of vigorous marine methane plumes located in areas of thin sediment cover and above the traces of deeply-penetrating forearc fault zones – but still presenting no thermal or chemical anomalies that are found in many other similar forearc environments and remain an unresolved paradox.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Effect of Na presence during CuInSe{sub 2} growth on stacking fault annihilation and electronic properties

While presence of Na is essential for the performance of high-efficiency Cu(In,Ga)Se{sub 2} thin film solar cells, the reasons why addition of Na by post-deposition treatment is superior to pre-deposition Na supply—particularly at low growth temperatures—are not yet fully understood. Here, we show by X-ray diffraction and electron microscopy that Na impedes annihilation of stacking faults during the Cu-poor/Cu-rich transition of low temperature 3-stage co-evaporation and prevents Cu homogeneity on a microscopic level. Lower charge carrier mobilities are found by optical pump terahertz probe spectroscopy for samples with remaining high stacking fault density, indicating a detrimental effect on electronic properties if Na is present during growth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗