SEARCH · Search NASA

Results for “Damage diagnosis”

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.

SourceResearch connectionFrom yearThrough year

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

15 records

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis

Machine learning (ML) models are gaining popularity in structural health monitoring (SHM) because of their ability to learn the complex relationship between damage and sensor data. However, the lack of sufficient experimental data for structures with different degrees of damage is a key problem in training ML models for SHM. This problem can be alleviated by using physics-based models to generate the required training data to build physics-informed ML (PIML) models for SHM. However, it takes significant computational effort to perform enough high-fidelity simulations of the diagnostic test. It is thus desirable to know whether the available computational resource budget should be expended on numerous low-fidelity physics simulations, or a small number of high-fidelity simulations, or their combination. In this paper, we investigate this aspect of generating adequate training data for PIML, by constructing multi-fidelity PIML models. We evaluate the performance of several PIML models, trained with different amounts of low-fidelity and high-fidelity data, in locating hidden cracks in concrete structures using a nonlinear dynamics-based diagnosis technique. Here, we find that high-fidelity physics simulations that do not cover the (test and damage) parameter space do not improve the performance of diagnostic PIML models built using data from many low-fidelity physics simulations.

42 ENGINEERING↗

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 OSTI2024

Wall heating by subcritical energetic electrons generated by the runaway electron avalanche source *

Abstract Subcritical energetic electrons (SEEs) produced by the runaway electron (RE) avalanche source at energies below the runaway threshold are found to be the primary contributor to surface heating of plasma-facing components (PFCs) during final loss events. This finding is supported by theoretical analysis, computational modeling with the Kinetic Orbit Runaway electrons Code (KORC), and qualitative agreement with DIII-D experimental observations. The avalanche source generates significantly more secondary electrons below the runaway threshold, which thermalize rapidly when well-confined. However, during a final loss event, the RE beam impacts the first wall, and SEEs are deconfined before they can thermalize. Additionally, because the energy deposition length decreases faster than energy, the deposited energy density, and thus the maximum PFC surface temperature change, is larger for SEEs than REs. KORC simulations employ an analytic first wall to model particle deconfinement onto a non-axisymmetric wall composed of individual tiles. PFC surface heating is calculated using a 1D model extended to include an energy-dependent deposition length scale. Simulations of DIII-D qualitatively agree with infrared (IR) imaging only when SEEs from the avalanche source are included. These results demonstrate that SEEs are the dominant contributor to PFC surface heating and indicate that the avalanche source plays a critical role in the PFC damage caused during final loss events. The prominence of SEEs also has important implications for interpreting IR imaging, one of the primary diagnostics for RE-wall interaction diagnosis, despite REs dominating the energy and current density. This result improves predictions of wall damage due to post-disruption REs to estimate material lifetime and design RE mitigation systems for ITER and future reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

An Improved Sampling and Baiting Method for Phytophthora tropicalis and P. heveae Detection in Macadamia integrifolia

Macadamia nuts are, economically, the second most important crop in the state of Hawai’i. A recent decline in yield and acreage has been attributed to insect damage and diseases such as Macadamia Quick Decline (MQD) caused by Phytophthora tropicalis and P. heveae. To develop an improved methodology for the diagnosis and treatment of MQD, investigations were undertaken to better understand the pathosystem of the disease. These investigations included sampling from multiple locations from sectioned trees utilizing two methods of tissue collection and isolations using two baiting techniques. The collection of tissue from the cambium and phloem of trees after scraping away the bark and in locations of recent or current sap exudation using a narrow diameter steel awl proved to be an efficient means for the molecular detection of the MQD pathogens from infected trees exhibiting MQD symptoms. In addition, a more efficient and cost-effective baiting method using apple puree was developed.

Plant Sciences↗

DOE OSTI2022

Nondestructive neutron imaging diagnosis of acidic gas reduction catalyst after 400-Hour operation in natural gas furnace

Residential natural gas furnaces are the primary space-heating devices in US homes, leading to substantial environmental impact caused by the acidic components in the furnace combustion gases. Here, to experimentally demonstrate acidic gas reduction in a furnace, a monolithic catalyst was fabricated and was called the AGR. A commercially-available condensing furnace was retrofitted with the AGR, and a 400-hour reliability and durability test was conducted. The results showed that the AGR significantly reduced acidic gases in the flue gas and produced condensate with neutral pH. Challenges were also revealed: inappropriate condensate drainage caused incomplete combustion and amorphous carbon deposits. To nondestructively survey the internal state of the AGR, neutron computed tomography (NCT) was employed to produce spatially resolved 2D and 3D representations of the 2-L, aged AGR component. The NCT results confirm the integrity of the AGR component, which consists of two blocks, without deformation or damage to AGR channels. The distribution of the particle accumulation in the middle of the top block was visibly heavier than the entrance and exit of the top block. The representative cross-section views revealed significant aggregation in the central region but not at the rim. The spatially resolved details provide in-depth diagnosis, evaluation, and understanding of the AGR. The insights can enable new AGR designs that realize a uniform and self-cleaning flow pattern, alleviate significant particle aggregation, and thus enhance AGR-enabled furnace performance. The neutron imaging method demonstrates the good potential that can diagnose faults and improve the design, optimization, and production processes of novel catalysts and other components or systems with heavy metal shell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Mitigation and Diagnosis of Pin-Hole Formation in Polymer Electrolyte Membrane Fuel Cells

In this work, we implement a calendering technique to flatten stray fibers within the gas diffusion media, thereby mitigating pin-hole formation in the hot-pressed MEAs. We have investigated the influence of calendering on the long-term durability for several types of gas diffusion electrodes (GDEs) using a combined chemical and mechanical accelerated stress test (AST). The calendered MEAs demonstrate an average AST lifetime improvement of 77% relative to the as-fabricated MEAs.

AMR↗

DOE OSTI2025

Advanced Diagnosis and Accelerated Testing of Balance of System Components for Utility Scale PV Installations: October 1, 2022-September 30, 2024

A study of the durability of PV Balance of System components was performed. Specifically, wire cable jackets and cable connectors were examined within the direct current (DC) PV Power Transmission Chain (PTC). Degraded and failed samples have been obtained from utility PV installations to provide feedback on the degradation modes and the related damage-enabling considerations in today's PV systems. An industry interface group (including system owners, system inspectors, component manufacturers, and test labs) was used to help identify and obtain field-failed samples, for feedback (including samples and experimental design), and to facilitate the subsequent dissemination of the results of this study. Samples were empirically studied using accelerated stress testing with steady-state conditions (cable jackets) in addition to combined-accelerated stress testing (cable jackets, connectors and uncapped connectors). Steady-state accelerated testing has been performed using at least one applied stressor (e.g. UV light) to aid understanding of jacket durability relative to its application. Component- and material-focused failure analysis was conducted to develop an understanding and advise the PV industry. In-depth characterization will be applied selectively to field- and artificially aged-samples, to gain scientific understanding of the structural, chemical, electrical, mechanical, and thermal properties enabling degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Physics-Based Limiter Redesign and Bit Performance Analysis at The Geysers

As part of a U.S. DOE Geothermal Technologies Office funding opportunity, Geysers Power Company, LLC (GPC), an indirect subsidiary of Calpine Corporation, partnered with Sandia National Labs, EGI at the University of Utah, and Texas A&M University to demonstrate increased drilling performance at The Geysers Geothermal Field. The performance target in the drilling demonstrations is at least a 25% improvement in rates of penetration, with increased footage on bottom for each bit coupled with increased bit life and time drilling. The project leverages advances in oil and gas drilling technologies including PDC bits, along with the physics-based limiter redesign techniques championed in drilling demonstrations conducted at the Utah FORGE geothermal site. The planned drilling demonstrations are being conducted as part of an existing drilling campaign intended to enhance reservoir utilization. The wells are typically drilled to the top of the reservoir with mud and then air-drilled to total depth (TD) through fractured zones at temperatures ≥ 450°F. A major goal of the project is to assess the effectiveness of implementing mechanical specific energy (MSE) and drilling dysfunction diagnosis and remediation in these challenging environments, as well as alternate rock reduction technologies. The first demonstration well has been completed, with 15 PDC bit runs in the 17.5”, 12.25” and 8.5” sections. Initial analysis shows ROP gains in all three sections, especially in the 17.5” and 12.25” sections, compared with conventional roller cone bit runs in the demonstration well and offset wells. However, in the 8.5” hole, wear and damage to the PDC bits resulted in relatively short bit runs. Analysis is underway to take advantage of the positive results and remediate the challenges. This paper provides updates on drilling activities conducted since the Phase 1 demonstration well at GDC-36 which was drilled from November 2023-January 2024. Additional analysis of the bit performance has been conducted. Furthermore, in subsequent wells drilled by GPC, PDC bits have been used extensively, building on the gains realized at GDC-36. GPC has continued to work with bit vendors to identify designs that last longer in the harsh, air-drilled 8.5” portions of the wells. Planning for the Phase 2 demonstration at Prati-44 is ongoing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

2024 Geothermal Rising Conference Poster

As part of a U.S. DOE Geothermal Technologies Office funding opportunity, Geysers Power Company, LLC, an indirect subsidiary of Calpine Corporation, partnered with Sandia National Labs, EGI at the University of Utah, and Texas A&M University to demonstrate increased drilling performance at The Geysers Geothermal Field. The performance target in the drilling demonstrations is at least a 25% improvement in rates of penetration with increased time on bottom for each bit. The planned drilling demonstrations are being conducted as part of an existing drilling campaign intended to enhance reservoir utilization. A major goal of the project is to assess the effectiveness of implementing mechanical specific energy (MSE) and drilling dysfunction diagnosis and remediation in these challenging environments, as well as alternate bit technologies. The first demonstration well has been completed, with a total of 15 PDC bit runs in the 17.5”, 12.25” and 8.5” sections. Initial analysis shows ROP gains in all three hole sections, especially in the 17.5” and 12.25” sections, compared with conventional roller cone bit runs in the demonstration well and offset wells. However in the 8.5” hole, wear and damage to the PDC bits resulted in relatively short bit runs. Analysis is underway to take advantage of the positive results and remediate the challenges.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Evaluation of Physics-Based Limiter Redesign Drilling and Alternative Bit Design at The Geysers

As part of a U.S. DOE Geothermal Technologies Office funding opportunity, Geysers Power Company, LLC, an indirect subsidiary of Calpine Corporation, partnered with Sandia National Labs, EGI at the University of Utah, and Texas A&M University to demonstrate increased drilling performance at The Geysers Geothermal Field. The performance target in the drilling demonstrations is at least a 25% improvement in rates of penetration with increased time on bottom for each bit. The project will leverage advances in oil and gas drilling technologies including PDC bits, along with the physics-based limiter redesign techniques championed in drilling demonstrations conducted at the Utah FORGE geothermal site. The planned drilling demonstrations are being conducted as part of an existing drilling campaign intended to enhance reservoir utilization. The wells are typically drilled to the top of the reservoir with mud and then air-drilled to total depth (TD) through fractured zones at temperatures ≥ 450°F. A major goal of the project is to assess the effectiveness of implementing mechanical specific energy (MSE) and drilling dysfunction diagnosis and remediation in these challenging environments, as well as alternate bit technologies. The first demonstration well has been completed, with a total of 15 PDC bit runs in the 17.5”, 12.25” and 8.5” sections. Initial analysis shows ROP gains in all three hole sections, especially in the 17.5” and 12.25” sections, compared with conventional roller cone bit runs in the demonstration well and offset wells. However in the 8.5” hole, wear and damage to the PDC bits resulted in relatively short bit runs. Analysis is underway to take advantage of the positive results and remediate the challenges.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. A recently proposed method to capture the rolling element bearing degradation provides the groundwork for new indicator families utilizing the generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the National Renewable Energy Laboratory Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox; hence the complexity of the vibration signals is similar to a real case. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.

condition monitoring↗

DOE OSTI2025

Detecting Short Circuits: Post Accident Electric Vehicle Battery Safety Check

Fast and accurate detection of soft short circuits (SCs) in the battery packs of damaged electric vehicles is needed by first responders and mechanics to mitigate the potential risk from battery fires that may occur hours, days, or weeks after an accident. Here, this paper presents an SC-detection algorithm for potentially damaged lithium-ion batteries that works quickly and without a priori knowledge of the battery-pack chemistry, capacity, state of charge, or state of health. The proposed universal SC-detection algorithm is designed to be implemented on an inexpensive handheld device that can connect to and monitor the voltages of all cells in a pack. Transient filtering and linear-quadratic state observation provide estimates of normalized SC current for every cell in the pack. Cells with SC-current estimates outside a sigma-based threshold are detected. Simulations, experiments, and electric vehicle (EV) crash data are used to verify the speed, sensitivity, and accuracy of the method, demonstrating 96% accurate detection of 0.0027 C SCs in under 1 h for 5S cell groups in the lab and no false positives for crashed Volkswagen, Chevrolet, and Tesla vehicles without SCs.

25 - ENERGY STORAGE↗