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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Bregon, Anibal↗
DOE OSTI2022

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Needs Assessment Framework for Storage Complexes Contributed to EDX (Work Product 2.1.c)

On September 30, 2021, a Needs Assessment Framework for Storage Complexes was completed for the SECARB-USA region by the Southern States Energy Board (SSEB) and The University of Texas at Austin Bureau of Economic Geology (UT-BEG). The Needs Assessment Framework for Storage Complexes documented a region-wide assessment to identify data needed to advance storage projects at sites of relevance to industrial, academic, and government stakeholders. The region-wide assessment addresses the needs of CO2 source, storage, and utilization stakeholders, finance and insurance institutions, state and local government agencies, local stakeholders at prospective storage complexes, environmental non-governmental organizations (NGOs), and others as identified by the Partners. This assessment was tested against various potential storage sites and modified to include site-specific issues, such as surface or pore space rights. The framework was provided to stakeholders for review and then used in other tasks, such as the ML initiative (Subtask 3.4) and storage complex readiness evaluation (Subtask 4.2). In accordance with the SOPO, this assessment satisfied completion of Work Product 2.1.b. The OSTI ID is 3015803, and the DOI link is https://doi.org/10.2172/3015803. On February 28, 2022, SSEB uploaded Work Product 2.1.b to NETL’s Energy Data eXchange (EDX). As such, Work Product 2.1.c is completed as documented below.

42 ENGINEERING↗
NASA NTRS1988

The N2-P3 complex of the evoked potential and human performance

The N2-P3 complex and other endogenous components of human evoked potential provide a set of tools for the investigation of human perceptual and cognitive processes. These multidimensional measures of central nervous system bioelectrical activity respond to a variety of environmental and internal factors which have been experimentally characterized. Their application to the analysis of human performance in naturalistic task environments is just beginning. Converging evidence suggests that the N2-P3 complex reflects processes of stimulus evaluation, perceptual resource allocation, and decision making that proceed in parallel, rather than in series, with response generation. Utilization of these EP components may provide insights into the central nervous system mechanisms modulating task performance unavailable from behavioral measures alone. The sensitivity of the N2-P3 complex to neuropathology, psychopathology, and pharmacological manipulation suggests that these components might provide sensitive markers for the effects of environmental stressors on the human central nervous system.

Odonnell, Brian F.↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
NASA NTRSDate not supplied

Overview of the Predictive Simulation Capability Element of the Plume Surface Interaction Project

As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The GCD PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC) Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper describes the PSC Element of the PSI Project as well as providing descriptions of recent accomplishments and remaining work. The overall structure of the PSC Element is broken down into four areas of focus. The first area is the Prediction of Plume Flow in low pressure environments. The development approach taken is the augmentation of the existing production-mode computational fluid dynamics (CFD) tool Loci/Chem, with targeted extensions necessary to accurately model rarefied conditions found in both Martian and Lunar applications. Production readiness and validation of predictive capability are the major objectives of this task. The second area is the effect of mixed continuum/rarefied flow on crater development and ejecta sheets. A new CFD application, Loci/GGFS (Gas Granular Flow Solver), is being developed which implements an Eulerian/Eulerian two-phase model of gas- and soil-phases in order to simulate the soil erosion, crater formation, and soil ejecta transport in a fully coupled simulation. This task seeks to verify Loci/GGFS is production-ready as well as perform validation studies to determine the degree of predictive capability achieved by Loci/GGFS. The third area is focused on the details and extension of particle phase modeling of soil. In this task, Discrete Element Modeling (DEM) techniques are used to perform direct simulations of complex soil particles under the action of forcing similar to that to be cause by PSI. The simulation results are then used to construct closures to the Eulerian model of the soil phase used by Loci/GGFS. The fourth area is gas-particle interaction modeling. In this task, experiments are being conducted as well as detailed simulation results are being studied to further understand the complexities of gas-particle interactions in dilute, intermediate, and high soil volume fraction regimes. Improved models of particle drag and the particle turbulent kinetic energy (PTKE) resulting from the interaction of gas flows within particle clouds are the objective of this task.

Jeff West↗
DOE OSTI2022

BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task

ABSTRACT Motivation Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model’s performance. Results We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets—BioASQ-7b, BioASQ-8b and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability and implementation BioADAPT-MRC is freely available as an open-source project at https://github.com/mmahbub/BioADAPT-MRC. Supplementary information Supplementary data are available at Bioinformatics online.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗
NASA NTRS2003

A Tutor That's Up to the Task

With assistance from NASA s Marshall Space Flight Center, a new breed of ITS for technical training and complex problem-solving has hit the market to provide students and trainees with the decision-making skills necessary to succeed to the next level. The Task Tutor Toolkit (T3), developed by Stottler Henke Associates, Inc., of San Mateo, California, is a generic tutoring system shell and scenario authoring tool that emulates expert instructors and lowers the cost and difficulty of creating scenario-based ITS for technical training. The functionality of Stottler Henke Associates T3 far exceeds that of traditional computer-based training systems, which test factual recall and narrow skills by prompting students to answer multiple-choice or fill-inthe- blank questions. The T3, on the contrary, lets students assess situations, generate solutions, make decisions, and carry out actions in realistically complex scenarios.At the beginning of each scenario, the T3 tutoring system presents a briefing that describes the situation and the goals the students should pursue. Each scenario contains a solution template that specifies a partially-ordered sequence of action patterns that match correct sequences of student actions. During each scenario, the built-in simulator notifies the tutoring system of each student action. The T3 uses this information to evaluate the student action by comparing it with the scenario s solution template and with error rules that detect incorrect actions.

Source record↗
NASA NTRS2006

Spitzer Space Telescope Sequencing Operations Software, Strategies, and Lessons Learned

The Space Infrared Telescope Facility (SIRTF) was launched in August, 2003, and renamed to the Spitzer Space Telescope in 2004. Two years of observing the universe in the wavelength range from 3 to 180 microns has yielded enormous scientific discoveries. Since this magnificent observatory has a limited lifetime, maximizing science viewing efficiency (ie, maximizing time spent executing activities directly related to science observations) was the key operational objective. The strategy employed for maximizing science viewing efficiency was to optimize spacecraft flexibility, adaptability, and use of observation time. The selected approach involved implementation of a multi-engine sequencing architecture coupled with nondeterministic spacecraft and science execution times. This approach, though effective, added much complexity to uplink operations and sequence development. The Jet Propulsion Laboratory (JPL) manages Spitzer s operations. As part of the uplink process, Spitzer s Mission Sequence Team (MST) was tasked with processing observatory inputs from the Spitzer Science Center (SSC) into efficiently integrated, constraint-checked, and modeled review and command products which accommodated the complexity of non-deterministic spacecraft and science event executions without increasing operations costs. The MST developed processes, scripts, and participated in the adaptation of multi-mission core software to enable rapid processing of complex sequences. The MST was also tasked with developing a Downlink Keyword File (DKF) which could instruct Deep Space Network (DSN) stations on how and when to configure themselves to receive Spitzer science data. As MST and uplink operations developed, important lessons were learned that should be applied to future missions, especially those missions which employ command-intensive operations via a multi-engine sequence architecture.

missions operations↗
NASA NTRS1996

Active Control of Noise Using Actuator/Sensor Arrays

Current research in smart structures is directed toward the integration of many actuators and sensors into a material. In this paper we investigate the possibility of using this instrumentation for active noise control from a vibrating structures. Current technology for reducing radiated sound is limited by the instrumentation for the control system. These control systems employ relatively small numbers of sensors and actuators. Hence, these control systems must rely on a model of the structure to estimate and control the global vibrations that contribute to the far field pressure. For complex, realistic structures the development of such a model is a formidable task. The model is a limiting factor in the continuing development of structural acoustics. In this paper we propose to increase the number of actuators and sensors of a smart material to offset the complexity of the model used for control design. The sensor arrays will be used to directly sense the shape of the structure rather than using a model of the structures to indirectly sense the shape of the structure. The actuator array is used to apply distributed forces to the structure, rather than using the structure itself as a load path. A control system for the active cancellation of sound is derived from standard control system methodologies.

Lindner, Douglas K.↗
DOE OSTI2024

Complexity: from Turbulence to Accelerators to Poetry

Though it may seem intuitive at times, the term 'complexity' is multifaceted, often encompassing various interacting aspects and components that follow local rules. This interaction leads to non-linearity, randomness, collective dynamics, hierarchy, and emergence. The phenomenological complexity found in large accelerators has recently been addressed by the Snowmass’21 Future Colliders Implementation Task Force (ITF). In this presentation, we will provide a concise overview of the mathematical constructs employed to assess hierarchical complexity, summarize the key findings from the ITF analysis, and offer a few examples demonstrating how this approach can be applied to analyze certain literaryclassics.

43 PARTICLE ACCELERATORS↗
NASA NTRS1986

Analytical comparison of transient and steady state visual evoked cortical potentials

To better describe the linear-dynamic properties of the human visual-cortical response system, transient and steady state Visual Evoked Response Potentials (VERP) were observed. The stimulus presentation device provided both the evoking stimulus (flickering or pulsing lights) and a video task display. The steady state stimulus was modulated by a complex, ten frequency, sum-of-sines, wave. The transient VERP was the time-locked average of the EEG to a series of narrow light pulses (pulse width of 10 msec). The Fourier transform of the averaged pulses had properties that approximate band limited white noise, i.e., a flat spectrum over the frequency region spanned by the 10 summed sines. The Fourier transform of both the steady state and the transient evoked potentials resulted in transfer that are equivalent and therefore comparable. To investigate the effects of task loading on evoked potentials, a grammatical reasoning task was provided. Results support the relevancy of continued application of a systems engineering approach for describing neurosensory functioning.

Junker, A. M.↗
NASA NTRS2001

Articulation Management for Intelligent Integration of Information

When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies and heterogeneity of representations, problems arise in combining multiple domains when the domains are merged. The knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. This heterogeneity problem can be eliminated by mediating the conflicts and managing the intersections of the domains. For interoperation and intelligent access to heterogeneous information, the focus is on the intersection of the knowledge, since intersection will define the required articulation rules. An algebra over domain has been proposed to use articulation rules to support disciplined manipulation of domain knowledge resources. The objective of a domain algebra is to provide the capability for interrogating many domain knowledge resources, which are largely semantically disjoint. The algebra supports formally the tasks of selecting, combining, extending, specializing, and modifying Components from a diverse set of domains. This paper presents a domain algebra and demonstrates the use of articulation rules to link declarative interfaces for Internet and enterprise applications. In particular, it discusses the articulation implementation as part of a production system capable of operating over the domain described by the IDL (interface description language) of objects registered in multiple CORBA servers.

Maluf, David A.↗
DOE OSTI2019

Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy

The third Sandia Fracture Challenge illustrated the geometric and material uncertainties presented by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Particularly, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This method allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.

316L stainless steel↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

97 MATHEMATICS AND COMPUTING↗