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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Interrogating the Effects of Hydrogen on the Behavior of Planar Deformation Bands in Austenitic Stainless Steel

The effects of internal hydrogen on the deformation microstructures of 304L austenitic stainless steel have been characterized using electron backscattered diffraction (EBSD), transmission Kikuchi diffraction (TKD), high-resolution scanning transmission electron microscopy (HRSTEM), and nanoprobe diffraction. Samples, both thermally precharged with hydrogen and without thermal precharging, were subjected to tensile deformation of 5 and 20 pct true strain followed by multiple microscopic interrogations. Internal hydrogen produced widespread stacking faults within the as-forged initially unstrained material. While planar deformation bands developed with tensile strain in both the hydrogen-precharged and non-precharged material, the character of these bands changed with the presence of internal hydrogen. As shown by nanobeam diffraction and HRSTEM observations, in the absence of internal hydrogen, the bands were predominantly composed of twins, whereas for samples deformed in the presence of internal hydrogen, ε -martensite became more pronounced and the density of deformation bands increased. For the 20 pct strain condition, α '-martensite was observed at the intersection of ε -martensite bands in hydrogen-precharged samples, whereas in non-precharged samples α '-martensite was only observed along grain boundaries. We hypothesize that the increased prevalence of α '-martensite is a secondary effect of increased ε -martensite and deformation band density due to internal hydrogen and is not a signature of internal hydrogen itself.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. Here, we also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Incorporating Full Elastodynamic Effects and Dipping Fault Geometries in Community Code Verification Exercises for Simulations of Earthquake Sequences and Aseismic Slip (SEAS)

ABSTRACT Numerical modeling of earthquake dynamics and derived insight for seismic hazard relies on credible, reproducible model results. The sequences of earthquakes and aseismic slip (SEAS) initiative has set out to facilitate community code comparisons, and verify and advance the next generation of physics-based earthquake models that reproduce all phases of the seismic cycle. With the goal of advancing SEAS models to robustly incorporate physical and geometrical complexities, here we present code comparison results from two new benchmark problems: BP1-FD considers full elastodynamic effects, and BP3-QD considers dipping fault geometries. Seven and eight modeling groups participated in BP1-FD and BP3-QD, respectively, allowing us to explore these physical ingredients across multiple codes and better understand associated numerical considerations. With new comparison metrics, we find that numerical resolution and computational domain size are critical parameters to obtain matching results. Codes for BP1-FD implement different criteria for switching between quasi-static and dynamic solvers, which require tuning to obtain matching results. In BP3-QD, proper remote boundary conditions consistent with specified rigid body translation are required to obtain matching surface displacements. With these numerical and mathematical issues resolved, we obtain excellent quantitative agreements among codes in earthquake interevent times, event moments, and coseismic slip, with reasonable agreements made in peak slip rates and rupture arrival time. We find that including full inertial effects generates events with larger slip rates and rupture speeds compared to the quasi-dynamic counterpart. For BP3-QD, both dip angle and sense of motion (thrust versus normal faulting) alter ground motion on the hanging and foot walls, and influence event patterns, with some sequences exhibiting similar-size characteristic earthquakes, and others exhibiting different-size events. These findings underscore the importance of considering full elastodynamics and nonvertical dip angles in SEAS models, as both influence short- and long-term earthquake behavior and are relevant to seismic hazard.

Geochemistry & Geophysics↗
NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗
DOE OSTI2020

Deformation behavior and phase transformation of nanotwinned Al/Ti multilayers

We report nanotwinned Al/Ti multilayers have exhibited size-dependent microstructure evolution and high strength. However, their deformation mechanisms are less well understood. In this work, we investigated the deformation mechanisms of nanotwinned Al/Ti multilayers with FCC/HCP layer interfaces by using in situ micropillar compression tests. Nanotwinned Al/Ti multilayers exhibit compressive strength up to 2.4 GPa and good work hardening capability. Post-compression TEM analyses reveal high-density stacking faults and the HCP-to-FCC phase transformations in Ti. Molecular dynamics simulations elucidate the mechanisms of deformation induced phase transformation in Ti and the influence of collective movement of partial dislocations on the deformability of Al/Ti multilayers.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Characterization of the High-Speed-Stage Bearing Skidding of Wind Turbine Gearboxes Induced by Dynamic Electricity Grid Events: Preprint

Bearing behavior is an important factor for wind turbine drivetrain reliability. Extreme loads and dynamic excitations pose challenges to the bearing design and therefore its performance. Excessive skidding of the bearing rollers should be avoided because it can cause scuffing failures. Excitations coming from wind and the electricity grid can subject the drivetrain to fluctuating torque and nontorque loads. Wind-induced excitations have been investigated predominantly in literature. However, modern wind turbines are subjected more and more to grid-induced loads because of stricter electricity grid regulations. For example, during fault-ride-through events, turbines are required to stay connected for a longer period of time during the grid failure. This work investigates the influence of electrically induced excitations on the skidding behaviour of the tapered roller bearings on the high-speed stage of a wind turbine gearbox. This skidding behaviour during dynamic events is described as a potential bearing failure initiator by many researchers; however, only limited full-scale dynamic testing is documented. Therefore, a dedicated gridloss-type event is defined in the paper and conducted in a dynamometer test on a full-scale wind turbine nacelle. During the event, a complete electricity grid failure is simulated while the turbine is at rated speed and predefined torque levels. Particular focus is on the characterization of the high-speed shaft tapered roller bearing slip behavior. Strain-gauge bridges in grooves along the circumference of the outer ring are used to characterize the bearing load zone in detail. It is shown that during the torque reversals of the transient event, roller slip can be induced. This indicates the potential of the applied load case to go beyond the preload of the tapered roller bearing. Furthermore, the relation between the applied torque and skidding level is studied.

17 WIND ENERGY↗
DOE OSTI2016

Characterization of the High-Speed-Stage Bearing Skidding of Wind Turbine Gearboxes Induced by Dynamic Electricity Grid Events

Bearing behavior is an important factor for wind turbine drivetrain reliability. Extreme loads and dynamic excitations pose challenges to the bearing design and therefore its performance. Excessive skidding of the bearing rollers should be avoided because it can cause scuffing failures. Excitations coming from wind and the electricity grid can subject the drivetrain to fluctuating torque and nontorque loads. Wind-induced excitations have been investigated predominantly in literature. However, modern wind turbines are subjected more and more to grid-induced loads because of stricter electricity grid regulations. For example, during fault-ride-through events, turbines are required to stay connected for a longer period of time during the grid failure. This work investigates the influence of electrically induced excitations on the skidding behaviour of the tapered roller bearings on the high-speed stage of a wind turbine gearbox. This skidding behaviour during dynamic events is described as a potential bearing failure initiator by many researchers; however, only limited full-scale dynamic testing is documented. Therefore, a dedicated gridloss-type event is defined in the paper and conducted in a dynamometer test on a full-scale wind turbine nacelle. During the event, a complete electricity grid failure is simulated while the turbine is at rated speed and predefined torque levels. Particular focus is on the characterization of the high-speed shaft tapered roller bearing slip behavior. Strain-gauge bridges in grooves along the circumference of the outer ring are used to characterize the bearing load zone in detail. It is shown that during the torque reversals of the transient event, roller slip can be induced. This indicates the potential of the applied load case to go beyond the preload of the tapered roller bearing. Furthermore, the relation between the applied torque and skidding level is studied.

17 WIND ENERGY↗
DOE OSTI2023

Downhole Sensing and Event-Driven Sensor Fusion for Depth-of-Cut Based Autonomous Fault Response and Drilling Optimization

Achieving robust and efficient drilling is a critical part of reducing the cost of geothermal energy exploration and extraction. Drilling performance is often evaluated using one or more of three key metrics: depth of cut (DOC), rate of penetration (ROP), and mechanical specific energy (MSE). All three of these quantities are related to each other. DOC refers to the depth a bit penetrates into rock during drilling. This is an important quantity for estimating bit behavior. ROP is the simply the DOC multiplied by the rotational rate, and represents how quickly the drill bit is advancing through the ground. ROP is often the parameter used for drilling control and optimization. Finally, MSE provides insight into drilling efficiency and rock type. MSE calculations rely on ROP, drilling force, and drilling torque. Surface-based sensors at the top of the drill are often used to measure all these quantities. However, top-hole measurements can deviate substantially from the behavior at the bit due to lag, vibrations, and friction. Therefore, relying only on top-hole information can lead to suboptimal drilling control. In this work, we describe recent progress towards estimating ROP, DOC, and MSE using down-hole sensing. We assume down-hole measurements of torque, weight-on-bit (WOB). Our hypothesis is that these measurements can provide more rapid and accurate measures of drilling performance. We show how a multi-layer perceptron (MLP) machine learning algorithm can provide rapid and accurate performance when evaluated on experimental data taken from Sandia’s Hard Rock Drilling Facility. In addition, we implement our algorithms on an embedded system intended to emulate a bottom-hole-assembly for sensing and estimation. Our experimental results show that DOC can be estimated accurately and in real-time. These estimates when combined with measurements for rotary speed, torque, and force can provide improved estimates for ROP and MSE. These results have the potential to enable better drilling assessment, improved control, and extended component lifetimes.

15 GEOTHERMAL ENERGY↗
NASA NTRS1991

Representing functions/procedures and processes/structures for analysis of effects of failures on functions and operations

Current qualitative device and process models represent only the structure and behavior of physical systems. However, systems in the real world include goal-oriented activities that generally cannot be easily represented using current modeling techniques. An extension of a qualitative modeling system, known as functional modeling, which captures goal-oriented activities explicitly is proposed and how they may be used to support intelligent automation and fault management is shown.

Malin, Jane T.↗
NASA NTRS1994

Automated knowledge-base refinement

Over the last several years, we have developed several systems for automatically refining incomplete and incorrect knowledge bases. These systems are given an imperfect rule base and a set of training examples and minimally modify the knowledge base to make it consistent with the examples. One of our most recent systems, FORTE, revises first-order Horn-clause knowledge bases. This system can be viewed as automatically debugging Prolog programs based on examples of correct and incorrect I/O pairs. In fact, we have already used the system to debug simple Prolog programs written by students in a programming language course. FORTE has also been used to automatically induce and revise qualitative models of several continuous dynamic devices from qualitative behavior traces. For example, it has been used to induce and revise a qualitative model of a portion of the Reaction Control System (RCS) of the NASA Space Shuttle. By fitting a correct model of this portion of the RCS to simulated qualitative data from a faulty system, FORTE was also able to correctly diagnose simple faults in this system.

Mooney, Raymond J.↗
NASA NTRS1995

Formal development of a clock synchronization circuit

This talk presents the latest stage in formal development of a fault-tolerant clock synchronization circuit. The development spans from a high level specification of the required properties to a circuit realizing the core function of the system. An abstract description of an algorithm has been verified to satisfy the high-level properties using the mechanical verification system EHDM. This abstract description is recast as a behavioral specification input to the Digital Design Derivation system (DDD) developed at Indiana University. DDD provides a formal design algebra for developing correct digital hardware. Using DDD as the principle design environment, a core circuit implementing the clock synchronization algorithm was developed. The design process consisted of standard DDD transformations augmented with an ad hoc refinement justified using the Prototype Verification System (PVS) from SRI International. Subsequent to the above development, Wilfredo Torres-Pomales discovered an area-efficient realization of the same function. Establishing correctness of this optimization requires reasoning in arithmetic, so a general verification is outside the domain of both DDD transformations and model-checking techniques. DDD represents digital hardware by systems of mutually recursive stream equations. A collection of PVS theories was developed to aid in reasoning about DDD-style streams. These theories include a combinator for defining streams that satisfy stream equations, and a means for proving stream equivalence by exhibiting a stream bisimulation. DDD was used to isolate the sub-system involved in Torres-Pomales' optimization. The equivalence between the original design and the optimized verified was verified in PVS by exhibiting a suitable bisimulation. The verification depended upon type constraints on the input streams and made extensive use of the PVS type system. The dependent types in PVS provided a useful mechanism for defining an appropriate bisimulation.

Miner, Paul S.↗
NASA NTRS2011

Parametric Testing of Launch Vehicle FDDR Models

For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.

Schumann, Johann↗
DOE OSTI2020

Advanced Geomechanical Model to Predict the Impact of CO2-Induced Microstructural Alterations on the Cohesive-Frictional Behavior of Mt. Simon Sandstone

We investigated the influence of CO2-induced geochemical reactions on the cohesive-frictional properties of host rock within the context of CO2 storage in a saline aquifer and focused on the Mt. Simon sandstone. The research objective was to model geo-mechanical changes due to host rock exposure to CO2-saturated brine while accounting for heterogeneity, double-scale porosity, and granular structure. We formulated a three-level multi-scale model for host rocks. We conducted scanning electron microscopy analyses to probe the microstructure and grid nanoindentation to measure the mechanical response. We derived new nonlinear strength upscaling solutions to correlate the effective strength characteristics and the macroscopic yield surface to the micro-structure at the nano-, micro-, and meso-scales. Specifically, our theoretical model links CO2-induced microstructural alterations to a reduction in the size of the yield surface, and a drop in the value of the friction coefficient. In turn, regarding the Illinois Basin Decatur Project, the CO2-induced drop in friction coefficient is linked to an increase in the risk of fault slip and a higher probability of induced microseismicity during and after the end of CO2 underground injection operations. The theoretical model presented is essential for the geo-mechanical modeling of CO2 underground injection operations at multiple length-scales.

58 GEOSCIENCES↗
NASA NTRS1998

A New Real - Time Fault Detection Methodology for Systems Under Test

The purpose of this research is focussed on the identification/demonstration of critical technology innovations that will be applied to various applications viz. Detection of automated machine Health Monitoring (BM, real-time data analysis and control of Systems Under Test (SUT). This new innovation using a High Fidelity Dynamic Model-based Simulation (BFDMS) approach will be used to implement a real-time monitoring, Test and Evaluation (T&E) methodology including the transient behavior of the system under test. The unique element of this process control technique is the use of high fidelity, computer generated dynamic models to replicate the behavior of actual Systems Under Test (SUT). It will provide a dynamic simulation capability that becomes the reference truth model, from which comparisons are made with the actual raw/conditioned data from the test elements.

Johnson, Roger W.↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
NASA NTRS1992

From pilot's associate to satellite controller's associate

Associate technology is an emerging engineering discipline wherein intelligent automation can significantly augment the performance of man-machine systems. An associate system is one that monitors operator activity and adapts its operational behavior accordingly. Associate technology is most effectively applied when mapped into management of the human-machine interface and display-control loop in typical manned systems. This paper addresses the potential for application of associate technology into the arena of intelligent command and control of satellite systems, from diagnosis of onboard and onground of satellite systems fault conditions, to execution of nominal satellite control functions. Rather than specifying a specific solution, this paper draws parallels between the Pilot's Associate concept and the domain of satellite control.

Neyland, David L.↗
NASA NTRS1993

The effects of history and predictive information on the ability of the transport aircraft pilot to predict an alert

The early detection of a developing aircraft-subsystem fault has the potential to lessen its ultimate severity. The lack of capability for such early detection is becoming critical in the aviation community. In the commercial sector, for example, twin-engine aircraft are being used for extended transport operations over water. One method to decrease the severity of a developing problem is to predict its behavior and to take appropriate corrective action. In order to investigate pilots' ability to predict events, an experiment was conducted where eighteen airline pilots predicted the time to an alert using three different displays of dials and three different time profile complexities. The three displays of dials were as follows: standard, resembling current aircraft dial presentations; history, indicating the value five seconds in the past; and predictive, indicating the value five seconds into the future. The time profiles describing the behavior of the parameter consisted of constant velocity profiles, decelerating profiles, and accelerating then decelerating profiles. Although pilots indicated that they preferred the predictive dial, the objective data did not support its use. The objective data did show that the time profiles had the most significant effect on performance in estimating the time to an alert.

Trujillo, Anna C.↗