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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF)

Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases.First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those of the simple models .The Pearson's test results show that the association strength between the measured and the predicted values of the proposed model EMD-MLP is the strongest. These results show the following: (a) compared with other related, recent studies, the prediction accuracy of the hybrid model EMD- MLP proposed in this research is the best hybrid model; (b) the proposed hybrid model (EMD-MLP) attains superior performance compared with simple models.

Djeddou, Messaoud↗
DOE OSTI2021

Evaluating Utility-Scale PV-Battery Hybrids in an Operational Model for the Bulk Power System

Systems that combine solar photovoltaic and battery energy storage technologies (PV-BES) are increasingly being proposed and deployed on the bulk power system. The operations and value of PV-BES systems have been extensively studied from the project developer's perspective through analyses that maximize plant-level revenue. However, PV-BES hybrids' operational characteristics are seldom studied from the perspective of bulk power system operators, who seek to optimize the performance of a suite of generation and storage assets that are connected via the transmission network. This work presents modeling approaches for representing and evaluating PV-BES hybrids in a model that optimizes operations across the bulk power system. Its novel contributions include demonstrating a technique to modify a unit commitment and dispatch model to represent the operational synergies of PV-BES hybrids. In particular, we describe the challenges and an approach for representing so-called DC-coupled PV-BES - which utilize a single bi-directional inverter - as a dispatchable resource in a commercial, production cost model (PCM), PLEXOS. We demonstrate this technique in a PCM study of the Los Angeles Department of Water and Power (LADWP) test system, by replacing existing PV and battery generators on the test system with our PV-BES hybrids. We then pursue scenario analysis that is designed to isolate the various drivers of operational strategies for DC-coupled PV-BES hybrids, including the nature of coupling, PV penetration on the system, and varying inverter loading ratios (or degrees of over-sizing of the PV field). Results from the analysis include utilization profiles for the PV DC energy across available pathways, dispatch profiles for the battery component, and the hybrid technologies' impacts on system-wide production costs. The approach presented in this paper can be used in any PCM that is looking to study PV-BES hybrids as a resource in different power system configurations and services.

14 SOLAR ENERGY↗
DOE OSTI2023

Modelling and Optimizing CSP-PV Hybrid Systems Using the Hybrid Optimization and Performance Platform (HOPP)

The hybridization of concentrating solar power (CSP) with thermal energy storage (TES), photovoltaics (PV), and electrochemical battery energy storage systems (BESS) has the potential to provide continuous, high-capacity-factor energy production at a lower cost than a PV-BESS or CSP with TES alone. Because of the system complexity of CSP technology, it is challenging to evaluate the technological and financial performance of a CSP-PV hybrid system without detailed modeling of annual operations. To address this challenge, we have developed a modeling framework for evaluating the performance and financial viability of CSP systems hybridized with PV and BESS technologies. This modeling effort incorporates CSP tower and trough configurations into an existing modeling tool recently developed by NREL, the Hybrid Optimization and Performance Platform (HOPP). This paper provides a brief overview of our methodology, as well as an example case study. CSP with TES hybridized with PV provides the best benefit-to-cost ratio compared to the other simulated technology combinations. However, for the conditions considered, this configuration only increases the benefit-to-cost ratio by about 1% compared to the CSP with TES configuration. The PV-BESS system provides the lowest benefit-to-cost ratio compared to the other configurations explored because of the relatively low capacity credit received by the system.

Hamilton, William (ORCID:0000000244654239)↗
NASA NTRS1984

Charging and hybridization in the finite cluster model

Cluster wavefunctions which have appropriate hybridization and polarization lead to reasonable properties for the interaction of an adsorbate with a solid surface. However, for Al clusters, it was found that the atomic change distribution is not uniform. The finite cluster size leads to changes not representative for an extended system. This effect appears to be dependent on the particular materials being studied; it does not occur in all cases.

Bauschlicher, C. W., Jr.↗
NASA NTRS1991

Numerical investigations with a hybrid isentropic-sigma model. I - Normal-mode characteristics. II - The inclusion of moist processes

The normal-mode characteristics of baroclinically amplifying disturbances were numerically investigated in a series of adiabatic simulations by a hybrid isentropic-sigma model, demonstrating the effect of coupling an isentropic-coordinate free atmospheric domain with a sigma-coordinate PBL on the normal-mode characteristics. Next, the normal-mode model was modified by including a transport equation for water vapor and adiabatic heating by condensation. Simulations with and without a hydrological component showed that the overall effect of latent heat release is to markedly enhance cyclogenesis and frontogenesis.

Pierce, R. B.↗
NASA NTRS2016

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Hybrid Systems Diagnosis↗
NASA NTRS1974

Computational study of nonlinear plasma waves

A low-noise plasma simulation model is developed and applied to a series of linear and nonlinear problems associated with electrostatic wave propagation in a one-dimensional, collisionless, Maxwellian plasma, in the absence of magnetic field. It is demonstrated that use of the hybrid simulation model allows economical studies to be carried out in both the linear and nonlinear regimes with better quantitative results, for comparable computing time, than can be obtained by conventional particle simulation models, or direct solution of the Vlasov equation. The characteristics of the hybrid simulation model itself are first investigated, and it is shown to be capable of verifying the theoretical linear dispersion relation at wave energy levels as low as .000001 of the plasma thermal energy. Having established the validity of the hybrid simulation model, it is then used to study the nonlinear dynamics of monochromatic wave, sideband instability due to trapped particles, and satellite growth.

Matsuda, Y.↗
DOE OSTI2023

A hybrid analytical and numerical model for cross-over and performance decay in a unit cell vanadium redox flow battery

Developing an accurate and efficient model for cross-over is critical for improving the long-term performance of redox flow batteries (RFBs). Here, this work presents a hybrid analytical and numerical model that combines a two-dimensional analytical solution to the active species, a one-dimensional analytical model for cross-over mechanisms, and a zero-dimensional numerical model for outlet concentrations of reactants. By comparing with experiment over 41 cycles (ca. 144 h), the model reported a mean voltage difference of 0.0089 V, mean time difference of 14 s per cycle (of 3.5 h), maximum relative difference for capacity and energy of 1.34% and 1.63%, respectively. The predicted mean concentrations for V 2+ , V 3+ , and VO$_{2}^{+}$ in membrane are 65%~77% of measured values from the literature. Upon validation, the model reproduced behaviors in electrolyte imbalance similar to those observed in experiments and numerical models, and revealed the control of cross-over, self-discharge, stoichiometry of side reactions, and Coulombic efficiency on electrolyte imbalance. The model also demonstrates excellent computational efficiency for simulating 41 cycles (around 37,000 points) within 3~4 s. The demonstrated efficiency and accuracy for predicting cross-over and its impacts on voltage, capacity&energy decay, membrane concentrations, and electrolyte imbalance makes it a reliable tool for optimizing RFBs’ long-term performance.

25 ENERGY STORAGE↗
NASA NTRS2000

Topographic Phase Recovery from Stacked ERS Interferometry and a Low-Resolution Digital Elevation Model

A hybrid approach to topographic recovery from ERS interferometry is developed and assessed. Tropospheric/ionospheric artifacts, imprecise orbital information, and layover are key issues in recovering topography and surface deformation from repeat-pass interferometry. Previously, we developed a phase gradient approach to stacking interferograms to reduce these errors and also to reduce the short-wavelength phase noise (see Sandwell arid Price [1998] and Appendix A). Here the method is extended to use a low-resolution digital elevation model to constrain long-wavelength phase errors and an iteration scheme to minimize errors in the computation of phase gradient. We demonstrate the topographic phase recovery on 16-m postings using 25 ERS synthetic aperture radar images from an area of southern California containing 2700 m of relief. On the basis of a comparison with 81 GPS monuments, the ERS derived topography has a typical absolute accuracy of better than 10 m except in areas of layover. The resulting topographic phase enables accurate two-pass, real-time interferometry even in mountainous areas where traditional phase unwrapping schemes fail. As an example, we form a topography-free (127-m perpendicular baseline) interferogram spanning 7.5 years; fringes from two major earthquakes and a seismic slip on the San Andreas Fault are clearly isolated.

Sandwell, David T.↗
NASA NTRS2011

Hybrid Adaptive Flight Control with Model Inversion Adaptation

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Nguyen, Nhan↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Simulation of Non-Acoustic Combustion Instability in a Hybrid Rocket Motor

A transient model of a hybrid motor was formulated to study the cause and elimination of non-acoustic combustion instability. The transient model was used to simulate four key tests out of a series of seventeen hybrid motor tests conducted by Thiokol, Rocketdyne and Martin Marietta at NASA/Marshall Space Flight Center (NASA/MSFC). These tests were performed under the Hybrid Propulsion Technology for Launch Vehicle Boosters (HPTLVB) program. The first test resulted in stable combustion. The second test resulted in large-amplitude, 6.5 Hz chamber pressure oscillations that gradually damped away by the end of the test. The third test resulted in large-amplitude, 7.5 Hz chamber pressure oscillations that were sustained throughout the test. The seventh test resulted in the elimination of combustion instability with the installation of an orifice immediately upstream of the injector. The formulation and implementation of the model are the scope of this presentation. The current model is an independent continuation of modeling presented previously by joint Thiokol-Rocketdyne collaborators Boardman, Hawkins, Wassom, and Claflin. The previous model simulated an unstable IR&D hybrid motor test performed by Thiokol. There was very good agreement between the model and the test data. Like the previous model, the current model was developed using Matrix-x simulation software. However, the tests performed at NASA/MSFC under the HPTLVB program were actually simulated. In the current model, the hybrid motor consisting of the liquid oxygen (LOX) injector, the multi-port solid fuel grain and the nozzle was simulated. Also, simulated in the model was the LOX feed system consisting of the tank, venturi, valve and feed lines. All components of the hybrid motor and LOX feed system are treated by a lumped-parameter approach. Agreement between the results of the transient model and the actual test data was very good. This agreement between simulated and actual test data indicated that the combustion instability in the hybrid motor was due to two causes. The first cause was a LOX feed system of insufficient stiffness. The second cause was a LOX injector with an impedance or pressure drop that was too low to provide damping against the feed system oscillations. Also, it was discovered that testing with a new grain of solid fuel sustained the combustion instability. However, testing with a used grain of solid fuel caused the combustion instability to gradually decay.

Rocker, Marvin↗
NASA NTRS1999

Simulation of Non-Acoustic Combustion Instability in a Hybrid Rocket Motor

A transient model of a hybrid motor was formulated to study the cause and elimination of non-acoustic combustion instability. The transient model was used to simulate four key tests out of a series of seventeen hybrid motor tests conducted by Thiokol, Rocketdyne and Martin Marietta at NASA/Marshall Space Flight Center (NASAIMSFC). These tests were performed under the Hybrid Propulsion Technology for Launch Vehicle Boosters (HPTLVB) program. The first test resulted in stable combustion. The second test resulted in large-amplitude, 6.5 Hz chamber pressure oscillations that gradually damped away by the end of the test. The third test resulted in large-amplitude, 7.5 Hz chamber pressure oscillations that were sustained throughout the test. The seventh test resulted in the elimination of combustion instability with the installation of an orifice immediately upstream of the injector. The formulation and implementation of the model are the scope of this presentation. The current model is an independent continuation of modeling presented previously by joint Thiokol-Rocketdyne collaborators Boardman, Hawkins, Wassom, and Claflin. The previous model simulated an unstable IR&D hybrid motor test performed by Thiokol. There was very good agreement between the model and the test data. Like the previous model, the current model was developed using Matrix-x simulation software. However, the tests performed at NASA/MSFC under the HPTLVB program were actually simulated. In the current model, the hybrid motor consisting of the liquid oxygen (LOX) injector, the multi-port solid fuel grain and the nozzle was simulated. Also, simulated in the model was the LOX feed system consisting of the tank, venturi, valve and feed lines. All components of the hybrid motor and LOX feed system are treated by a lumped-parameter approach. Agreement between the results of the transient model and the actual test data was very good. This agreement between simulated and actual test data indicated that the combustion instability in the hybrid motor was due to two causes. The first cause was a LOX feed system of insufficient stiffness. The second cause was a LOX injector with an impedance or pressure drop that was too low to provide damping against the feed system oscillations. Also, it was discovered that testing with a new grain of solid fuel sustained the combustion instability. However, testing with a used grain of solid fuel caused the combustion instability to gradually decay.

Rocker, Marvin↗
NASA NTRS1975

Aerodynamic characteristics of a large-scale hybrid upper surface blown flap model having four engines

Data are presented from an investigation of the aerodynamic characteristics of large-scale wind tunnel aircraft model that utilized a hybrid-upper surface blown flap to augment lift. The hybrid concept of this investigation used a portion of the turbofan exhaust air for blowing over the trailing edge flap to provide boundary layer control. The model, tested in the Ames 40- by 80-foot Wind Tunnel, had a 27.5 deg swept wing of aspect ratio 8 and 4 turbofan engines mounted on the upper surface of the wing. The lift of the model was augmented by turbofan exhaust impingement on the wind upper-surface and flap system. Results were obtained for three flap deflections, for some variation of engine nozzle configuration and for jet thrust coefficients from 0 to 3.0. Six-component longitudinal and lateral data are presented with four engine operation and with the critical engine out. In addition, a limited number of cross-plots of the data are presented. All of the tests were made with a downwash rake installed instead of a horizontal tail. Some of these downwash data are also presented.

Carros, R. J.↗
DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Ice Accretion Roughness Variations on a Hybrid CRM65-Midspan Wing Model

Ice accretion roughness measurements were performed in the Icing Research Tunnel (IRT) at NASA Glenn Research Center for the Hybrid CRM65-Midspan model in a range of icing conditions. The Hybrid CRM65-Midspan model was chosen for this investigation because 1) the model exhibits high sweep relative to models previously explored in the roughness investigations, 2) the model has leading edge characteristics similar to wing shapes currently used in mid-size commercial airliners, and 3) the sweep and thickness ratios relate better to hybrid lifting body designs for N+2 and N+3 vehicles than other models available. The investigation consisted of multiple sets of tests which focused on 1) 0-angle of attack cases replicating the conditions employed by Anderson et al. (1998) using both Appendix C and SLD cloud conditions, 2) cases based on the “Max Scallop” case by Broeren et al. (2016) and a “High Temperature” case with cloud properties similar to the “Max Scallop” case. Additional tests were performed 1) based on the “Max Scallop” case with variations in freestream static temperature and 2) using test section speeds near 10,000-hold flight conditions. The point clouds were characterized using the approach of McClain and Kreeger (2013) for the ice roughness variations and using the approach of McClain (2016) for the mean ice thickness variations. The resulting roughness and mean thickness variations generally follow the temporal scaling previously identified using on airfoil models without sweep, but the collapse of the time progression profiles is not as tight as found for past measurements on models without sweep. LEWICE and modified panel-method predictions were used explore spatial roughness variations and to compare to the roughness correlations developed by McClain et al. (2021) for the “Max Scallop” cases.

Icing↗