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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Functional Fault Model Development Process to Support Design Analysis and Operational Assessment

A functional fault model (FFM) is an abstract representation of the failure space of a given system. As such, it simulates the propagation of failure effects along paths between the origin of the system failure modes and points within the system capable of observing the failure effects. As a result, FFMs may be used to diagnose the presence of failures in the modeled system. FFMs necessarily contain a significant amount of information about the design, operations, and failure modes and effects. One of the important benefits of FFMs is that they may be qualitative, rather than quantitative and, as a result, may be implemented early in the design process when there is more potential to positively impact the system design. FFMs may therefore be developed and matured throughout the monitored system's design process and may subsequently be used to provide real-time diagnostic assessments that support system operations. This paper provides an overview of a generalized NASA process that is being used to develop and apply FFMs. FFM technology has been evolving for more than 25 years. The FFM development process presented in this paper was refined during NASA's Ares I, Space Launch System, and Ground Systems Development and Operations programs (i.e., from about 2007 to the present). Process refinement took place as new modeling, analysis, and verification tools were created to enhance FFM capabilities. In this paper, standard elements of a model development process (i.e., knowledge acquisition, conceptual design, implementation & verification, and application) are described within the context of FFMs. Further, newer tools and analytical capabilities that may benefit the broader systems engineering process are identified and briefly described. The discussion is intended as a high-level guide for future FFM modelers.

Verification↗
NASA NTRS2023

Considerations for Software Fault Prevention and Tolerance

Mission or safety-critical spaceflight systems should be developed to both reduce the likelihood of software faults pre-flight and to detect/mitigate the effects of software errors should they occur in-flight. New data is available that categorizes software errors from significant historic spaceflight software incidents with implications and considerations to better develop and design software to both minimize and tolerate these most likely software failures.

Avionics↗
DOE OSTI2017

On the micromechanics of slip events in sheared, fluid-saturated fault gouge

We used a three-dimensional discrete element method coupled with computational fluid dynamics to study the poromechanical properties of dry and fluid-saturated granular fault gouge. The granular layer was sheared under dry conditions to establish a steady state condition of stick-slip dynamic failure, and then fluid was introduced to study its effect on subsequent failure events. The fluid-saturated case showed increased stick-slip recurrence time and larger slip events compared to the dry case. Particle motion induces fluid flow with local pressure variation, which in turn leads to high particle kinetic energy during slip due to increased drag forces from fluid on particles. The presence of fluid during the stick phase of loading promotes a more stable configuration evidenced by higher particle coordination number. Our coupled fluid-particle simulations provide grain-scale information that improves understanding of slip instabilities and illuminates details of phenomenological, macroscale observations.

58 GEOSCIENCES↗
DOE OSTI2018

Broadband (0–4 Hz) Ground Motions for a Magnitude 7.0 Hayward Fault Earthquake With Three‐Dimensional Structure and Topography

Abstract We performed fully deterministic broadband (0–4 Hz) high‐performance computing ground motion simulations of a magnitude 7.0 scenario earthquake on the Hayward Fault (HF) in the San Francisco Bay Area of Northern California. Simulations consider average one‐dimensional (1‐D) and three‐dimensional (3‐D) anelastic structure with flat and topographic free surfaces. Ground motion intensity measures (GMIMs) for the 3‐D model display dramatic differences across the HF due to geologic heterogeneity, with low wave speeds east of the HF amplifying motions. The median GMIMs agree well with Ground Motion Prediction Equations (GMPEs); however, the 3‐D model generates more scatter than the 1‐D model. Ratios of 3‐D/1‐D GMIMs from the same source allow isolation of path and site effects for the 3‐D model. These ratios show remarkably similar trends as site‐specific factors for the GMPE predictions, suggesting that wave propagation effects in our 3‐D simulations are on average consistent with empirical data.

58 GEOSCIENCES↗
DOE OSTI2020

Identification of potential CO 2 leakage pathways and mechanisms in oil reservoirs using fault tree analysis

Geological storage of CO 2 technologies has become an important and effective way to reduce the greenhouse gas emissions, especially when it is combined with CO 2 enhanced oil recovery (EOR), which can not only trap CO 2 but also enhance oil recovery. However, the risk of CO 2 leakage has always been a prominent issue. In this paper, the mechanisms and pathways of CO 2 leakage during geological storage in oil reservoirs were analyzed using fault tree analysis (FTR). Besides, monitoring technologies were discussed and deployed in a CO 2 EOR demonstration project. The analysis results showed that the sealing failures of oil producer and CO2 injector wells, like well cement failure and casing failure, are the main reasons for the CO 2 leakage, which has been observed in the oil field monitoring project. The monitoring results indicated that there is no large-scale CO 2 leakage, while relatively high and abnormal CO 2 concentration in soil gas near some wellbores are observed, which indicates there is some leakage of CO 2 through incomplete cement ring and well casing string. FTR results provide guidelines for monitoring and preventing of CO 2 leakage during geological storage in oil reservoirs. Finally, the near-surface monitoring methods, especially the soil gas monitoring technologies, can effectively detect the leakage of CO 2 , and are a proper method for CO 2 leakage monitoring.

58 GEOSCIENCES↗
DOE OSTI2019

Large-Scale Molecular Dynamics Simulation Studies on Deformation of Ni Nanowires: Surface Profile, Defects and Stacking Fault Width Analysis

We report large-scale molecular dynamics simulation deformation studies of Ni nanowire (NW) of size 100 Å (x) × 1000 Å (y) × 100 Å (z) comprising of 925,965 atoms. Surface and internal defects are introduced to study their effect on the surface profile, strength, fracture behavior and deformation mechanisms. Tensile tests have been carried out at a temperature of 10 K and at a strain rate of 10{sup 8} s{sup −1}. Periodic boundary condition is applied along the loading direction (y). Peak strength of 23 GPa is observed in the perfect NW, and the strength decreases with defects. The surface profiles of the deformed NWs show intrusion and extrusion regions corresponding to slip steps with wider valleys in the defect NWs. Several intrinsic and extrinsic parallel stacking faults (SFs) are generated after yielding by slip occurring on {111} planes. The calculated SF widths are in the range of 0.85-2.57 nm in the perfect NW. The dislocations are mainly Shockley partial dislocations of type 1/6 〈112〉, and Schmid’s factor (m) obtained is 0.471. Twinning dislocation of types 1/9 〈221〉 and 1/18 〈172〉 is also observed in the SFs. The density of Shockley partial dislocations is observed to be the maximum in all the NWs.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Stacking fault energies of face-centered cubic concentrated solid solution alloys

We report the stacking fault energy (SFE) for a series of face-centered cubic (fcc) equiatomic concentrated solid solution alloys (CSAs) derived as subsystems from the NiCoFeCrMn and NiCoFeCrPd high entropy alloys based on ab initio calculations. At low temperatures, these CSAs display very low even negative SFEs, indicating that hexagonal close-pack ( hcp ) is more energy favorable than fcc structure. The temperature dependence of SFE for some CSAs is studied. With increasing temperature, a hcp -to- fcc transition is revealed for those CSAs with negative SFEs, which can be attributed to the role of intrinsic vibrational entropy. The analysis of the vibrational modes suggests that the vibrational entropy arises from the high frequency states in the hcp structure that originate from local vibrational mode. Furthermore, our results underscore the importance of vibrational entropy in determining the temperature dependence of SFE for CSAs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Towards stacking fault energy engineering in FCC high entropy alloys

Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many years as a key intrinsic mechanical property, its inference via experimental observations or prediction using, for example, computationally intensive first-principles methods is challenging. This difficulty precludes the explicit use of SFE as an alloy design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict the SFE in the fcc CoCrFeMnNiV-Al high-entropy alloy space. The best-performing ML model is capable of accurately predicting the SFE of arbitrary compositions within this 7-element system. Finally, this efficient model along with a recently developed model to estimate intrinsic strength of fcc HEAs is used to explore the strength–SFE Pareto front, predicting new-candidate alloys with particularly interesting mechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A theoretical calculation of stacking fault energy of Ni alloys: The effects of temperature and composition

Combining cluster expansion (CE) method with one dimensional axial Ising model, this work investigated the effects of alloying elements and configurational variations due to temperature on the stacking fault energy (SFE) of FCC Ni binary alloys. Ensembles of large numbers of atomistic structures, each with more than ~400 atoms, were generated to consider sufficient long-range chemical disorder and temperature effects due to configurational entropy. Here, a Monte Carlo Metropolis algorithm was used to generate these structures, whose energies were then evaluated based on the effective cluster interactions obtained from CE. As a baseline, this work had shown the SFE of pure Ni and Al to be 127 mJ/m 2 and 137 mJ/m 2 , respectively, which agreed with the experimental values of 125 mJ/m 2 and 150 mJ/m 2 reported in the literature. Additions of Al, Ti, Cr and Co to pure Ni were found to decrease the SFE to different extents. Although temperature does not strongly influence the SFE of the FCC Ni-Al and Ni-Cr binary alloys, it can lead to significant changes to the SFE of the FCC Ni-Ti and Ni-Co alloys. While effects of temperature and composition on SFE observed in this work were calculated from binary Ni alloys, the general trends are nonetheless expected to be valid in the γ phase of multicomponent Ni superalloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Benchmark study of a new simplified DFN model for shearing of intersecting fractures and faults

It is challenging to quantitatively predict shearing of intersecting fractures/faults because of dynamic frictional contacts accompanied by possible nonlinear rock deformation. To address such challenges, a new conceptual model—the simplified DFN model—was proposed and validated by Hu et al. 46 to use major paths (MPs) to represent complicated DFNs for calculation of shearing. In this work, we conducted a benchmark study for three examples that involve different levels of complexity of intersecting fractures, and correspondingly different numbers of MPs. The codes and software that were used in the benchmark cover a range of continuum, discontinuum and hybrid numerical methods: NMM (LBNL), FLAC3D (LBNL), GBDEM (KIGAM), FRACOD (DynaFrax), and CASRock (CAS). The general consistency between DFN and MP cases as predicted by all the codes/software demonstrates that major paths can be used to simplify the geometry of DFNs in a wide range of software. Disagreement in results made by some software and potential future improvements are discussed. We show that (1) shearing of one or multiple major fractures can be reduced if there are multiple smaller intersecting fractures in that area, which is a useful basis for understanding and controlling induced seismicity and merits further analysis, and (2) the agreement achieved in the benchmark examples provide confidence that the simplified DFN model is a promising conceptual model that can be used for different types of numerical approaches and software for simplifying the analysis of the shearing of intersecting fractures and faults.

58 GEOSCIENCES↗
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2018

From random stacking faults to polytypes: A 12-layer NiSn 4 polytype

NiSn 4 grows in diffusion couples (Ni plates with electrodeposited Sn) in a temperature range from room temperature up to 353 K. Previously, the crystal structure of NiSn 4 grown at ambient temperature was identified to be an intermediate stacking variant between the orthorhombic PtSn 4 -type and the tetragonal β-IrSn 4 -type structure, with a slight tendency towards the tetragonal type. Now it is reported that NiSn 4 forming at 333 K and above, exhibits a 12-layer polytype (space group P4 2 /nbc, a = b = 6.250 Å, c = 69.00 Å) due to ordered incorporation of stacking faults into the structure. Here, this NiSn 4 structure was derived by comparison of its diffraction patterns with those of the exhaustively generated NiSn 4 polytypes. First-principles calculations on a series of selected NiSn 4 polytypes demonstrate that the experimentally observed polytype is energetically favoured as compared to others.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nanoscale fluctuation of stacking fault energy strengthens multi-principal element alloys

Chemical randomness and the associated energy fluctuation are essential features of multi-principal element alloys (MPEAs). Due to these features, nanoscale stacking fault energy (SFE) fluctuation is a natural and independent contribution to strengthening MPEAs. However, existing models for conventional alloys (i.e., alloys with one principal element) cannot be applied to MPEAs. The extreme values of SFEs required by such models are unknown for MPEAs, which need to calculate the nanoscale volume relevant to the SFE fluctuation. In the present work, we developed an analytic model to evaluate the strengthening effect through the SFE fluctuation, profuse in MPEAs. The model has no adjustable parameters, and all parameters can be determined from experiments and ab initio calculations. Further, this model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation. It generally applies to MPEAs in random states and with chemical short-range order.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated fault detection and diagnosis deployment Internet of Things solution for building energy system

The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50–52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. Here, this study reviews existing IoT solutions developed for building energy–related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

42 ENGINEERING↗
DOE OSTI2015

Exploiting data representation for fault tolerance

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product's result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Microstructure and mechanical properties of bulk highly faulted fcc/hcp nanostructured cobalt microstructures

Nanostructured cobalt powders with an average particle size of 50 nm were synthesized using a polyol method and subsequently consolidated by spark plasma sintering (SPS). SPS experiments performed at 650 °C with sintering times ranging from 5 to 45 min under a pressure of 100 MPa, yielded to dense bulk nanostructured cobalt (relative density greater than 97%). X-ray diffraction patterns of the as-prepared powders showed only a face centered cubic (fcc) crystalline phase, whereas the consolidated samples exhibited a mixture of both fcc and hexagonal close packed (hcp) phases. Transmission electron microscopy observations revealed a lamellar substructure with a high density of nanotwins and stacking faults in every grain of the sintered samples. Room temperature compression tests, carried out at a strain rate of 10{sup −3} s{sup −1}, yielded to highest strain to fracture values of up to 5% for sample of holding time of 15 min, which exhibited a yield strength of 1440 MPa, an ultimate strength as high as 1740 MPa and a Young's modulus of 205 GPa. The modulus of elasticity obtained from the nanoindentation tests, ranges from 181 to 218 GPa. The lowest modulus value of 181 GPa was obtained for the sample with the highest sintering time (45 min), which could be related to mass density loss as a consequence of trapped gases releasing. - Highlights: • Co nanopowder (50 nm) was prepared by reduction in polyol medium. • SPS was used to process bulk nanostructured Co specimens. • Microstructures were made of intricate fcc/hcp, along with nanotwins and SFs. • High strengths and moderate compressive ductility were obtained. • Deformation mechanisms related to complex interplay of different length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Formation and Growth of Stacking Fault Tetrahedra in Ni via Vacancy Aggregation Mechanism

Using molecular dynamics simulations, the formation and growth of stacking fault tetrahedra (SFT) are captured by vacancy cluster diffusion and aggregation mechanisms in Ni. The vacancytetrahedron acts as a nucleation point for SFT formation. Simulations show that perfect SFT can grow to the next size perfect SFT via a vacancy aggregation mechanism. The stopping and range of ions in matter (SRIM) calculations and transmission electron microscopy (TEM) observations reveal that SFT can form farther away from the initial cascade-event locations, indicating the operation of diffusion-based vacancy-aggregation mechanism.

74 ATOMIC AND MOLECULAR PHYSICS↗