Physics and Hybrid Model Approaches for Prognostics and Decision Making
Explore the source record for details and available documents.
Prognostics↗
SEARCH · Engineering Papers
Results for “hybrid model”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Saturn's Magnetosphere Interaction with Titan for T9 Encounter: 3D Hybrid Modeling and Comparison with CAPS Observations
Global dynamics of ionized and neutral gases in the environment of Titan plays an important role in the interaction of Saturn s magnetosphere with Titan. Several hybrid simulations of this problem have already been done (Brecht et al., 2000; Kallio et al., 2004; Modolo et al., 2007a; Simon et al., 2007a, 2007b; Modolo and Chanteur, 2008). Observational data from CAPS for the T9 encounter (Sittler et al., 2009) indicates an absence of O(+) heavy ions in the upstream that change the models of interaction which were discussed in current publications (Kallio et al., 2004; Modolo et al., 2007a; Simon et al., 2007a, 2007b; Ma et al., 2007; Szego et al., 2007). Further analysis of the CAPS data shows very low density or even an absence of H(+) ions in upstream. In this paper we discuss two models of the interaction of Saturn s magnetosphere with Titan: (A) high density of H(+) ions in the upstream flow (0.1/cu cm), and (B) low density of H(+) ions in the upstream flow (0.02/cu cm). The hybrid model employs a fluid description for electrons and neutrals, whereas a particle approach is used for ions. We also take into account charge-exchange and photoionization processes and solve self-consistently for electric and magnetic fields. The model atmosphere includes exospheric H(+), H(2+), N(2+)and CH(4+) pickup ion production as well as an immobile background ionosphere and a shell distribution for active ionospheric ions (M(sub i)=28 amu). The hybrid model allows us to account for the realistic anisotropic ion velocity distribution that cannot be done in fluid simulations with isotropic temperatures. Our simulation shows an asymmetry of the ion density distribution and the magnetic field, including the formation of Alfven wing-like structures. The results of the ion dynamics in Titan s environment are compared with Cassini T9 encounter data (CAPS).
Lipatov, A. S.↗
Hybrid Modeling Improves Health and Performance Monitoring
Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.
Source record↗
Hybrid Modeling of Three-Phase Grid-Supporting Inverters for Dynamic Studies
Grid technologies connected by power electronic converter (PEC) interfaces continually implement grid support functions mandated by grid codes and standards. The transition to converter-based generation demands precise PEC models to assess system dynamics, which have been previously overlooked in conventional power systems. This study proposes a hybrid method for analyzing grid-connected three-phase PEC dynamics with the IEEE standard 1547-2018 Volt-VAr mode that combines physics and data-driven techniques. The physics model reflects the PEC’s internal behavior, whereas the data-driven modeling technique evaluates the grid-supporting capabilities of the smart PEC. The system identification approach is used to generate dynamic PEC models based on changing grid voltage and measured current injected into the grid by the PEC. In the Volt-VAr support mode, a detailed topological model including switches is utilized to compare the goodness-of-fit of the extracted hybrid dynamic model. The results demonstrate that the hybrid PEC model in the Volt-VAr mode accurately matches the dynamics with the topological model.
Subedi, Sunil↗
Hybrid Modeling of a Circulating Water Pump Motor
To achieve high capacity factors, the nuclear fleet has relied on labor-intensive and time-consuming operation and preventive maintenance programs for plant systems. Manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies, along with the time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies that can eliminate unnecessary preventive maintenance activities by deploying risk-informed predictive maintenance, enabling the transition to a technology-centric business model.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Effects of Kinetic Processes in Shaping Io's Global Plasma Environment: A 3D Hybrid Model
The global dynamics of the ionized and neutral gases in the environment of Io plays an important role in the interaction of Jupiter s corotating magnetospheric plasma with Io. Stationary simulations of this problem have already been done using the magnetohydrodynamics (MHD) and the electrodynamics approaches. One of the major results of recent simplified two-fluid model simulations [Saur, J., Neubauer, F.M., Strobel, D.F., Summers, M.E., 2002. J. Geophys. Res. 107 (SMP5), 1-18] was the production of the structure of the double-peak in the magnetic field signature of the Io flyby. These could not be explained before by standard MHD models. In this paper, we present a hybrid simulation for Io with kinetic ions and fluid electrons. This method employs a fluid description for electrons and neutrals, whereas for ions a particle approach is used. We also take into account charge-exchange and photoionization processes and solve self-consistently for electric and magnetic fields. Our model may provide a much more accurate description for the ion dynamics than previous approaches and allows us to account for the realistic anisotropic ion velocity distribution that cannot be done in fluid simulations with isotropic temperatures. The first results of such a simulation of the dynamics of ions in Io s environment are discussed in this paper. Comparison with the Galileo IO flyby results shows that this approach provides an accurate physical basis for the interaction and can therefore naturally reproduce all the observed salient features.
Lipatov, Alexander S.↗
Hybrid Modeling for Complex Systems Health Management
The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.
PINNS↗
An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring
The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Hybrid modeling of the formation and structure of thin current sheets in the magnetotail
Hybrid simulations are used to investigate the formation of a thin current sheet inside the plasma sheet of a magnetotail-like configuration. The initial equilibrium is subjected to a driving electric field which is qualitatively similar to what would be expected from solar wind driving. As a result, a new current sheet with the thickness of approximately the ion inertial length is formed. The current density inside the current sheet region is supplied largely by the electrons. Ion acceleration in the cross-tail direction is absent as the driving electric field fails to penetrate into the equatorial region.
Hesse, Michael↗
Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics
This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.
Physics-Informed Machine Learning↗
Solutions of contact problems by the assumed stress hybrid model
A method was developed for contact problems which may be either frictional or frictionless and may involve extensive sliding between deformable bodies. It was based on an assumed stress hybrid approach and on an incremental variational principle for which the Euler's equations of the functional include the equilibrium and compatibility conditions at the contact surface. The tractions at an assumed contact surface were introduced as Lagrangian multipliers in the formulation. It was concluded from the results of several example solutions that the extensive sliding contact between deformable bodies can be solved by the present method.
Kubomura, K.↗
Hybrid Modeling for Scenario-Based Evaluation of Failure Effects in Advanced Hardware-Software Designs
This paper describes an incremental scenario-based simulation approach to evaluation of intelligent software for control and management of hardware systems. A hybrid continuous/discrete event simulation of the hardware dynamically interacts with the intelligent software in operations scenarios. Embedded anomalous conditions and failures in simulated hardware can lead to emergent software behavior and identification of missing or faulty software or hardware requirements. An approach is described for extending simulation-based automated incremental failure modes and effects analysis, to support concurrent evaluation of intelligent software and the hardware controlled by the software
Malin, Jane T.↗
Traction free finite elements with the assumed stress hybrid model
An effective approach in the finite element analysis of the stress field at the traction free boundary of a solid continuum was studied. Conventional displacement and assumed stress finite elements were used in the determination of stress concentrations around circular and elliptical holes. Specialized hybrid elements were then developed to improve the satisfaction of prescribed traction boundary conditions. Results of the stress analysis indicated that finite elements which exactly satisfy the free stress boundary conditions are the most accurate and efficient in such problems. A general approach for hybrid finite elements which incorporate traction free boundaries of arbitrary geometry was formulated.
Kafie, Kurosh↗
Multiscale Hybrid Modeling of the Impact Response of the Earth's Magnetotail to Ionospheric O+ Outflow
Ionospheric outflow plays an important role in coupling the ionosphere with the solar wind-magnetosphere system. Previous multi-fluid MHD studies explored the global influence of oxygen ions of ionospheric origin (O+) on magnetospheric dynamics. A detailed exploration of the interaction of ionospheric ions with the magnetotail requires kinetic treatment for ions. We perform a self-consistent investigation of these processes with a three-dimensional space-time adaptive hybrid code, HYPERS, powered by an intelligent Event-driven Multi-Agent Planning System (EMAPS). By comparing simulations with and without outflow we conclude that oxygen ions, flowing from the ionosphere through the lobes into the tail or directly entering the inner magnetosphere, are able to significantly modify the magnetotail configuration and induce X-points and current sheet structures at locations where magnetic reconnection does not occur in a simulation without outflow, potentially very close to the Earth. This finding may have implications for interpreting substorms and magnetotail reconnection events observed for southward magnetic field simultaneously with significant contents of oxygen ions of ionospheric origin.
multiscale↗
Bridging scales: A hybrid model to simulate vascular tumor growth and treatment response
Not Available
Engineering↗
Deep hybrid modeling of chemical process: Application to hydraulic fracturing
Not Available
Bangi, Mohammed Saad Faizan↗
Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model
Not Available
Li, Jinyang (ORCID:000900036456354X)↗