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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fire-Induced Pressure Response and Failure Characterization of PCV/SCV/3013 Containers - Phase 3

Several Department of Energy (DOE) facilities have materials stored in robust, welded, stainless - steel containers with presumed fire - induced pressure response behaviors. Lack of test data related to fire exposure requires conservative safety analysis assumptions for container response at these facilities. This conservatism can in turn result in the implementation of challenging operational restrictions with costly nuclear safety controls. To help address this issue for sites that store DOE 3013 stainless - steel containers, a series of ten tests were undertaken at Sandia National Laboratories. The goal of this test series was to obtain the response behavior for various configurations of DOE 3013 containers with various payload compositions when exposed to one of two ASTM fire conditions. Key parameters measured in the test series included identification of failure - specific characteristics such as pressure, temperature, and whether or not a vessel was breached during a test . Numerous failure - specific characteristics were identified from the ten tests. This report describes the implementation and execution of the test series performed to identify these failure - specific characteristics. Discussions on the test configurations, payload compositions, thermal insults, and experimental setups are presented. Test results in terms of pressurization and vessel breach (or no - breach) are presented along with corresponding discussions for each test.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Advanced Detection of Wellbore Failure for Safe and Secure Utilization of Subsurface Infrastructure

The main goal of this project was to create a state-of-the-art predictive capability that screens and identifies wellbores that are at the highest risk of catastrophic failure. This capability is critical to a host of subsurface applications, including gas storage, hydrocarbon extraction and storage, geothermal energy development, and waste disposal, which depend on seal integrity to meet U.S. energy demands in a safe and secure manner. In addition to the screening tool, this project also developed several other supporting capabilities to help understand fundamental processes involved in wellbore failure. This included novel experimental methods to characterize permeability and porosity evolution during compressive failure of cement, as well as methods and capabilities for understanding two-phase flow in damaged wellbore systems, and novel fracture-resistant cements made from recycled fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FY23 Simulation of Elastic-Plastic Failure Propagation

This memo summarizes the simulation of ductile failure propagation work conducted under the ASC project “V&V of Ductile Failure” conducted during FY 23. Physically, the failure propagation consists of crack propagation in the material. In the numerical setting—specifically in a finite element model—propagation can be accomplished through element death when critical conditions occur locally at an element that is then deleted from the simulation. The validation of the finite element models is evaluated by direct comparison between the experimental and simulation results regarding the rate of crack growth and its influence on the load-deflection response of the specimens tested. This work considers two geometries that display stable crack propagation under displacement-controlled conditions. The first geometry consists of hat specimens loaded in compression with nominally identical geometries but made with three different materials: Steel A286, Al 7075-T651 and 304L stainless steel. The three materials represent a range of ductility values that affect the response and crack propagation within the specimen. The crack induced propagates under an essentially mode-II type of deformation. The second geometry consists of a pre-cracked 304L stainless steel compact tension test specimen loaded so as to induce a mode-I deformation at the crack.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗
DOE OSTI2023

Patterns and rates of soil movement and shallow failures across several small watersheds on the Seward Peninsula, Alaska

Abstract. Thawing permafrost can alter topography, ecosystems, and sediment and carbon fluxes, but predicting landscape evolution of permafrost-influenced watersheds in response to warming and/or hydrological changes remains an unsolved challenge. Sediment flux and slope instability in sloping saturated soils have been commonly predicted from topographic metrics (e.g., slope, drainage area). In addition to topographic factors, cohesion imparted by soil and vegetation and melting ground ice may also control spatial trends in slope stability, but the distribution of ground ice is poorly constrained and hard to predict. To address whether slope stability and surface displacements follow topographic-based predictions, we document recent drivers of permafrost sediment flux present on a landscape in western Alaska that include creep, solifluction, gullying, and catastrophic hillslope failures ranging in size from a few meters to tens of meters, and we find evidence of rapid and substantial landscape change on an annual timescale. We quantify the timing and rate of surface movements using a multi-pronged, multi-scalar dataset including aerial surveys, interannual GPS surveys, synthetic aperture radar interferometry (InSAR), and climate data. Despite clear visual evidence of downslope soil transport of solifluction lobes, we find that the interannual downslope surface displacement of these features does not outpace downslope displacement of soil in locations where lobes are absent (downslope movement means: 7 cm yr−1 for lobes over 2 years vs. 10 cm yr−1 in landscape positions without lobes over 1 year). Annual displacements do not appear related to slope, drainage area, or modeled total solar radiation but are likely related to soil thickness, and volumetric sediment fluxes are high compared to temperate landscapes of comparable bedrock lithology. Time series of InSAR displacements show accelerated movement in late summer, associated with intense rainfall and/or deep thaw. While mapped slope failures do cluster at slope–area thresholds, a simple slope stability model driven with hydraulic conductivities representative of throughflow in mineral and organic soil drastically overpredicts the occurrence of slope failures. This mismatch implies permafrost hillslopes have unaccounted-for cohesion and/or throughflow pathways, perhaps modulated by vegetation, which stabilize slopes against high rainfall. Our results highlight the breadth and complexity of soil transport processes in Arctic landscapes and demonstrate the utility of using a range of synergistic data collection methods to observe multiple scales of landscape change, which can aid in predicting periglacial landscape evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigating Various Failure Models on Commercial Silicon Carbide

Structures and components made from ceramic materials are often brittle and can fail by the unstable growth of existing flaws such as voids and cracks. There have been several failure criteria developed for ceramics, in the past and they are broadly categorized based on their dependency on crack geometry. The present work implements eight failure criteria using an open-source software package – srlife, which predicts the lifetime or failure probability of concentrated solar power (CSP) structural components. The present work also checks the viability of building a ceramic CSP receiver, by analyzing the reliability predictions from srlife for a SiC ceramic. The reliability predictions for a biaxial loading problem indicated the Shetty Mixed-Mode criterion gives the most conservative predictions. Whereas, in case of the CSP receiver, the predictions show that the co-planar strain energy criterion gives the most conservative predictions as it is agnostic towards the type of stress, and therefore, is not recommended to be used designing ceramic receivers.

Chaugule, Pawan (ORCID:0000000275706572)↗
DOE OSTI2023

PV Inverter Failures Cause AC Breaker Damage - Power Quality

This poster discusses how PV inverter failures cause AC breaker damage and is intended to help system owners identify power quality issues and origins, help inverter and/or circuit breaker manufacturers develop safer failure modes, and develop addendums to standards for failure testing of breakers and inverters.

arc↗
DOE OSTI2022

Predicting failure parameters of semiconductor devices subjected to stress conditions

A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.

Ahmed, Moinuddin↗
DOE OSTI2023

Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel

The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety is vital for the reliable operation of nuclear technologies. However, the TRISO fuel failure probabilities are small and the computational model is time consuming to evaluate them using traditional Monte Carlo-type approaches. In the paper, we present a multifidelity active learning approach to efficiently estimate small failure probabilities given an expensive computational model. Active learning suggests the next best training set for optimal subsequent predictive performance and multifidelity modeling uses cheaper low-fidelity models to approximate the high-fidelity model output. After presenting the multifidelity active learning approach, we apply it to efficiently predict TRISO failure probability and make comparisons to the reference results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Interim Development of new Class B Code Case with Variable Design Lifetime and enhanced design rules to guard against cyclic structural failure mode

This report summarizes updates on the ongoing development of the new American Society of Mechanical Engineers Section III, Division 5, Class B rules to address the potential failure modes of Class B components. An initial design-by-analysis approach and supporting design rules are presented. The elastic-perfectly plastic (EPP) analysis approach has been adopted for primary stress limit and ratcheting check to prevent the component failure against the structural failure mode against primary load and strain accumulation due to cyclic loads. The creep-fatigue damage assessment uses explicitly defined elastic follow-up calculated from the stress concentration region of given component. Damage fraction calculation uses a novel coupled approach to capture the influence of the elastic follow-up and provide adequate conservatism for Class B components. The proposed Class B rules does not use the stress classification approach and uses the combined loads to assess component.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An FEM-Based Peridynamic Model for Failure Analysis of Unidirectional Fiber-Reinforced Laminates

To predict the mixed damage modes of unidirectional fiber-reinforced polymer (FRP) laminates under dynamic loading, an FEM-based peridynamic model is introduced in this paper. Based on its geometric structure and material composition, a long fiber lamina is considered a transversely isotropic medium as a result of homogenization at the meso-scale. The laminated structure is modeled by stacking surface mesh layers with arbitrary fiber angles along the thickness direction. The peridynamic bonds between Gauss points connect the separated elements. These bonds are classified as inner-layer bonds and inter-layer bonds. To represent the anisotropy of a laminate, the micro-elastic modulus of the inner-layer and inter-layer bonds is calculated from the anisotropic engineering material constants separately. To capture complex failure behaviors of laminate structures, an empirical damage model is proposed for the tension/ compression breakage of peridynamic bonds. This damage model can control the in-plane and delamination failure process. Finally, benchmark tests are conducted to validate the elastic response of laminates under dynamic loading. In terms of damage analysis, the proposed model can capture the complex damage modes and resistive force of laminate structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Experimental investigation of probabilistic failure of SiC/SiC composite tubes under multiaxial loading

This paper presents an experimental investigation of the failure behavior of SiC fiber-reinforced SiC matrix (SiC/SiC) composite tubes under multiaxial loading. A new testing apparatus is designed to independently apply axial stress and internal pressure to SiC/SiC tubes. Acoustic emission (AE) is used to monitor the damage growth in the specimen. Based on the measured stress–strain response, a strain-based criterion is proposed to determine the proportional limit stress (PLS). The proposed strain-based criterion is compared with the PLS determined by the AE measurement. The PLS is determined for different loading ratios to form a multiaxial failure surface. By testing multiple replicates, the statistical variations of the PLS are determined. In conclusion, based on the experimental results, a mathematical model is developed to characterize the probability distribution of the PLS of SiC/SiC composites under multiaxial loading.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hydrogen-enhanced compatibility constraint for intergranular failure in FCC FeNiCoCrMn high-entropy alloy

The fundamental mechanism of hydrogen embrittlement was investigated in the high-entropy alloy FeNiCoCrMn using slow strain rate tensile tests with and without internal hydrogen. Hydrogen induced intergranular failure and reduced the average grain elongation parallel to the tensile axis, but also increased the local plasticity within grains. Here, the influence of hydrogen on plasticity establishes a compatibility constraint across grain boundaries, which results in failure along the hydrogen-weakened grain boundaries. This study is the first to directly confirm the presence of the hydrogen-enhanced compatibility constraint in a high-entropy alloy and highlights the importance of developing a physical understanding of grain-scale interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electrochemical-mechanical coupling failure mechanism of composite cathode in all-solid-state batteries

Composite cathode composed of active particles and solid electrolytes (SEs) can considerably enlarge the particle-SE contact areas and achieve high areal loadings in all-solid-state batteries (ASSBs). However, the challenging interfacial instability and particle damage problems remain unsolved. Herein, we establish a 3D electrochemical-mechanical coupled model to investigate the underlying failure mechanism by considering the governing electrochemical and physics processes. Micro-scale heterogeneous primary particles with random crystallographic orientation and size inside the LiNi 1/3 Co 1/3 Mn 1/3 O 2 (NCM111) secondary particle of the model result in the anisotropic Li diffusion and volume variation within the secondary particle, leading to significant nonuniformity of the Li concentration, and GPa-level stress distributions at primary particle boundaries, and finally causing the particle internal cracks. The particle volume shrinkage under the constraint of stiff Li 7 La 3 Zr 2 O 12 (LLZO) SE triggers the interface debonding (gap>50 nm) with increased interfacial impedance to degrade cell capacity. Higher C-rates result in larger residual stress (~100 MPa)/strain/debonding gap at dis-charging end, more likely to deteriorate the cell performance. Increasing the interfacial strength between the particle and SE can suppress the interface debonding but induces high stress (up to 10 GPa). In conclusion, results reveal the underlying mechanism of the electrochemical-mechanical coupling failure mechanism for composite cathode and provide promising guidance on the further improvement of a more robust composite cathode for ASSBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Intergranular ductile failure of materials with plastically heterogeneous grains

In several precipitation hardened alloys that are susceptible to intergranular ductile failure, precipitation does not always occur uniformly throughout the microstructure, and regions close to grain boundaries may remain precipitate-free. These precipitate-free zones (PFZs) in the material microstructures result in plastically heterogeneous grains, since PFZs are expected to have lower yield strength but higher strain-hardenability compared to the precipitate containing grain interior. Experimentally, the presence of PFZs in precipitation-hardened alloys has been associated with both increase and decrease in materials’ ductility, with or without significant change in the strength. Thus, to understand and rationalize the experimental observations, we have carried out extensive microstructure-based finite element calculations of intergranular ductile failure in materials under tensile loading conditions. In the calculations, both the grain boundaries and PFZs are discretely modeled, and a wide range of the values of yield strength, strain-hardenability and width of PFZs in the material microstructures are analyzed. Our results show that the effects of PFZs on the overall mechanical response of the material strongly depend on the values of yield strength and strain-hardenability of PFZs. Here, there exists an optimum combination of the values of these two parameters that can result in the overall ductility and tensile strength of the material microstructures with PFZs being greater than the microstructures without PFZs.

36 MATERIALS SCIENCE↗