Seasonal Maize yield forecasting in South and East African Countries using hybrid Earth observation models
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Benson Kipkemboi Kenduiywo↗
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.
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Hybrid Neural Network Modeling for Multiple Intersections along Signalized Arterials - Current Situation and Some New Results
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Wang, Hong↗
Prediction of Critical Heat Flux in Rod Bundles Using Tube-Based Hybrid Machine Learning Models in CTF
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Furlong, Aidan [North Carolina State University]↗
Quantum AI Based Enhanced Detection of Dementia
Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.
Bhowmik, Sounak [University of Tennessee, Knoxvill↗
Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems
Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.
DC–AC power systems↗
An Approach for Utilizing Power Flow Modeling for Simulations of Hybrid Electric Propulsion Systems
This paper describes an approach to creating simulations of the electric components for a hybrid electric propulsionsystem. The proposed modeling technique is based on power/load flow modeling and is designed to provide a modularframework that includes buses, lines, and other electrical components that can be connected together to form the electrical distribution system. The purpose of this paper is to detail an electric distribution system modeling technique and to demonstrate how these models may be integrated with turbomachinery simulations. These general modeling techniques were created to be utilized for system and control design studies. Additionally, steady-state and dynamic performance for a proposed model example is compared with data from a hardware in the loop simulation.
Power Flow↗
Interpretable Convolutional Learning Classifier System (C-LCS) for Higher Dimensional Datasets
The purpose of this paper is to devise an interpretable hybrid classification model for Convolutional Neural Networks (CNN) and a Learning Classifier System (LCS). The presented hybrid system integrates the fundamental attributes from both types of these classifiers. In the proposed hybrid model CNN works as an automatic feature extractor, and LCS works to provide interpretable rule-based classification results. Although LCS has limitations working on higher dimensional datasets, we resolve this limitation by using CNN as a feature extractor. The other concept of the non-interpretability of CNN is addressed by using the LCS rule. Furthermore, our experiment with higher dimensional datasets like CIFAR-10 and Fashion-MNIST shows that extended LCS provides comparable performance to the standard neural network model while also providing interpretable results. We named this extended LCS method Convolutional Learning Classifier Cystem (C-LCS).
Jelani Owens↗
Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model
In this work, a hybrid method coupling a pseudo-potential lattice Boltzmann model (LBM) and a pore network model (PNM) to simulate drying in porous media is proposed. Based on the watershed method, the porous medium is firstly decomposed into pore regions. According to the liquid–vapor phase distribution at a given time, the pore regions are further divided into four pore types, namely two-phase pores where a liquid–vapor interface exists, buffer pores next to the two-phase pores, single-liquid and single-vapor phase pores. The pseudo-potential LBM is used in the two-phase and buffer pores to simulate liquid drying and track the movement of the interfaces, while the single-phase PNM simulations are conducted in the buffer and single-phase pores to simulate vapor or liquid flow. LBM and PNM are coupled in the buffer pores through exchange of boundary information. The hybrid method is applied to simulate liquid drying in a porous medium. The whole-domain LBM simulation is considered as the reference solution to validate the hybrid method. Liquid saturation variation during the drying process and detailed phase and pressure distributions obtained by the two methods match quite well, demonstrating the accuracy of the hybrid method. For the specific case studied, the hybrid method saves more than 60% computational time compared to the whole-domain LBM simulation. In addition, the speedup of the hybrid method becomes more significant for a larger computational domain. In summary, the hybrid method developed in this work combines the accuracy of LBM and the efficiency of PNM to simulate drying in porous media at pore scale and can lead to significant reduction of computation time, thus allowing the pore-scale consideration of drying in larger porous systems.
36 MATERIALS SCIENCE↗
Quasi-Langrangian models of nascent thermals
The motions in and around an isolated thermal were studied and rising motion in the core, and sinking motion on the outside were found; while the circulation resembled that of a vortex ring. In an entity cloud model, cloudy thermal is tracked, in a Lagrangian fashion, as a discrete entity; the field of motion in and around the thermal is not explicitly simulated. Field of motion cloud models, in which the equations of motion are numerically integrated on an Eulerian grid were developed. It is shown that the great potential of a hybrid cloud model can combine the simplicity of the entity models with the generality and flexibility of the field-of-motion models. A key problem to be overcome in the development of a hybrid model is the formulation of a mathematical framework within which the cloud dynamics can be represented.
Rambaldi, S.↗
A Generic Two-Vector Model Predictive Control for Hybrid Multilevel Converters
In this article, a generic two-vector model predictive control (TV-MPC) strategy is proposed for the hybrid multilevel converters (HMCs). The proposed method selects two optimal voltage vectors among all the vector candidates using a geometric positioning approach to reduce the computational burden, which is a common issue in the existing MPC methods for HMC. Then duty cycles of the two selected vectors are optimized to minimize the current tracking error, such that the current tracking performance can be enhanced compared with the conventional MPC. In addition, the voltages of the floating dc capacitors can be balanced by evaluating all switching sequences that belong to the optimal voltage vectors with optimal duty cycles. Here, the concept of the proposed TV-MPC is generic and applicable for any HMCs. A typical HMC based on active neutral-point-clamped topology is adopted as a case study in this work. Comprehensive simulation and experimental studies are performed on an all silicon-carbide HMC prototype to validate the effectiveness of the proposed control scheme.
42 ENGINEERING↗
Reaction Wheel Disturbance Model Extraction Software - RWDMES
The RWDMES is a tool for modeling the disturbances imparted on spacecraft by spinning reaction wheels. Reaction wheels are usually the largest disturbance source on a precision pointing spacecraft, and can be the dominating source of pointing error. Accurate knowledge of the disturbance environment is critical to accurate prediction of the pointing performance. In the past, it has been difficult to extract an accurate wheel disturbance model since the forcing mechanisms are difficult to model physically, and the forcing amplitudes are filtered by the dynamics of the reaction wheel. RWDMES captures the wheel-induced disturbances using a hybrid physical/empirical model that is extracted directly from measured forcing data. The empirical models capture the tonal forces that occur at harmonics of the spin rate, and the broadband forces that arise from random effects. The empirical forcing functions are filtered by a physical model of the wheel structure that includes spin-rate-dependent moments (gyroscopic terms). The resulting hybrid model creates a highly accurate prediction of wheel-induced forces. It accounts for variation in disturbance frequency, as well as the shifts in structural amplification by the whirl modes, as the spin rate changes. This software provides a point-and-click environment for producing accurate models with minimal user effort. Where conventional approaches may take weeks to produce a model of variable quality, RWDMES can create a demonstrably high accuracy model in two hours. The software consists of a graphical user interface (GUI) that enables the user to specify all analysis parameters, to evaluate analysis results and to iteratively refine the model. Underlying algorithms automatically extract disturbance harmonics, initialize and tune harmonic models, and initialize and tune broadband noise models. The component steps are described in the RWDMES user s guide and include: converting time domain data to waterfall PSDs (power spectral densities); converting PSDs to order analysis data; extracting harmonics; initializing and simultaneously tuning a harmonic model and a wheel structural model; initializing and tuning a broadband model; and verifying the harmonic/broadband/structural model against the measurement data. Functional operation is through a MATLAB GUI that loads test data, performs the various analyses, plots evaluation data for assessment and refinement of analysis parameters, and exports the data to documentation or downstream analysis code. The harmonic models are defined as specified functions of frequency, typically speed-squared. The reaction wheel structural model is realized as mass, damping, and stiffness matrices (typically from a finite element analysis package) with the addition of a gyroscopic forcing matrix. The broadband noise model is realized as a set of speed-dependent filters. The tuning of the combined model is performed using nonlinear least squares techniques. RWDMES is implemented as a MATLAB toolbox comprising the Fit Manager for performing the model extraction, Data Manager for managing input data and output models, the Gyro Manager for modifying wheel structural models, and the Harmonic Editor for evaluating and tuning harmonic models. This software was validated using data from Goodrich E wheels, and from GSFC Lunar Reconnaissance Orbiter (LRO) wheels. The validation testing proved that RWDMES has the capability to extract accurate disturbance models from flight reaction wheels with minimal user effort.
Blaurock, Carl↗
Enhanced Self Tuning On-Board Real-Time Model (eSTORM) for Aircraft Engine Performance Health Tracking
A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. This report describes the development of a hybrid engine model for a propulsion gas turbine engine, which is the result of fusing two diverse modeling methodologies: a physics-based model approach and an empirical model approach. The report describes the process and methods involved in deriving and implementing a hybrid model configuration for a commercial turbofan engine. Among the intended uses for such a model is to enable real-time, on-board tracking of engine module performance changes and engine parameter synthesis for fault detection and accommodation.
Volponi, Al↗
Nonlinear Thermoelastic Model for SMAs and SMA Hybrid Composites
A constitutive mathematical model has been developed that predicts the nonlinear thermomechanical behaviors of shape-memory-alloys (SMAs) and of shape-memory-alloy hybrid composite (SMAHC) structures, which are composite-material structures that contain embedded SMA actuators. SMAHC structures have been investigated for their potential utility in a variety of applications in which there are requirements for static or dynamic control of the shapes of structures, control of the thermoelastic responses of structures, or control of noise and vibrations. The present model overcomes deficiencies of prior, overly simplistic or qualitative models that have proven ineffective or intractable for engineering of SMAHC structures. The model is sophisticated enough to capture the essential features of the mechanics of SMAHC structures yet simple enough to accommodate input from fundamental engineering measurements and is in a form that is amenable to implementation in general-purpose structural analysis environments.
Turner, Travis L.↗
Jovian Plasma Torus Interaction with Europa: 3D Hybrid Kinetic Simulation. First results
The hybrid kinetic model supports comprehensive simulation of the interaction between different spatial and energetic elements of the Europa-moon-magnetosphere system with respect to variable upstream magnetic field and flux or density distributions of plasma and energetic ions, electrons, and neutral atoms. This capability is critical for improving the interpretation of the existing Europa flyby measurements from the Galileo orbiter mission, and for planning flyby and orbital measurements, (including the surface and atmospheric compositions) for future missions. The simulations are based on recent models of the atmosphere of Europa (Cassidy etal.,2007;Shematovichetal.,2005). In contrast to previous approaches with MHD simulations, the hybrid model allows us to fully take into account the finite gyro radius effect and electron pressure, and to correctly estimate the ion velocity distribution and the fluxes along the magnetic field (assuming an initial Maxwellian velocity distribution for upstream background ions).Non-thermal distributions of upstream plasma will be addressed in future work. Photoionization,electron-impact ionization, charge exchange and collisions between the ions and neutrals are also included in our model. We consider two models for background plasma:(a) with O(++) ions; (b) with O(++) and S(++) ions. The majority of O2 atmosphere is thermal with an extended cold population (Cassidyetal.,2007). A few first simulations already include an induced magnetic dipole; however, several important effects of induced magnetic fields arising from oceanic shell conductivity will be addressed in later work.
Lipatov, A. S.↗
Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive
Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ( $n_{e0}$ , $T_{e0}$ , $I_p$ , $B_t$ , $R_0$ , $n_{\|}$ , $Z_{{\rm eff}}$ , $V_{{\rm loop}}$ and $P_{{\rm LHCD}}$ ) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to $\sim$ ms with high accuracy across the input parameter space.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Hybrid Solar Heat Generation Modelling and Cases: Preprint
Renewable thermal energy systems (RTES) harness renewable energy sources to provide services for space heating and cooling, district heating, domestic hot water, and industrial process heat (IPH). The use of low-pressure steam generated by the combustion of fossil fuels is common today to provide process heat for industrial facilities. Solar IPH (SIPH) technologies could economically replace the steam or heat needs at many industrial sites by providing high-temperature pressurized hot water, a heat transfer fluid (HTF) such as synthetic-oil, or direct steam (Kurup and Turchi, 2015). RTES could be hybridized with technology options or combined with the existing heat supply (e.g. fuels), to give options for targeted IPH application and the reduction of fuel consumption. This work has tested hybrid system modelling approaches. Initial results show when a natural gas (NG) burner that feeds an IPH application of 300°C, has both air and NG streams pre-heated with a solar field/RTES exit temperature of 180°C (via an HTF), a 13% NG offset is possible. NG offsets reach up to 26%, when the RTES exit temperatures are at 300°C for a given annual capacity factor of 24%. This can be even higher with addition of thermal energy storage (TES).
43 PARTICLE ACCELERATORS↗
Jovian Plasmas Torus Interaction with Europa. Plasma Wake Structure and Effect of Inductive Magnetic Field: 3D Hybrid Kinetic Simulation
The hybrid kinetic model supports comprehensive simulation of the interaction between different spatial and energetic elements of the Europa moon-magnetosphere system with respect to a variable upstream magnetic field and flux or density distributions of plasma and energetic ions, electrons, and neutral atoms. This capability is critical for improving the interpretation of the existing Europa flyby measurements from the Galileo Orbiter mission, and for planning flyby and orbital measurements (including the surface and atmospheric compositions) for future missions. The simulations are based on recent models of the atmosphere of Europa (Cassidy et al., 2007; Shematovich et al., 2005). In contrast to previous approaches with MHD simulations, the hybrid model allows us to fully take into account the finite gyroradius effect and electron pressure, and to correctly estimate the ion velocity distribution and the fluxes along the magnetic field (assuming an initial Maxwellian velocity distribution for upstream background ions). Photoionization, electron-impact ionization, charge exchange and collisions between the ions and neutrals are also included in our model. We consider the models with Oþ þ and Sþ þ background plasma, and various betas for background ions and electrons, and pickup electrons. The majority of O2 atmosphere is thermal with an extended non-thermal population (Cassidy et al., 2007). In this paper, we discuss two tasks: (1) the plasma wake structure dependence on the parameters of the upstream plasma and Europa's atmosphere (model I, cases (a) and (b) with a homogeneous Jovian magnetosphere field, an inductive magnetic dipole and high oceanic shell conductivity); and (2) estimation of the possible effect of an induced magnetic field arising from oceanic shell conductivity. This effect was estimated based on the difference between the observed and modeled magnetic fields (model II, case (c) with an inhomogeneous Jovian magnetosphere field, an inductive magnetic dipole and low oceanic shell conductivity).
Jovian magnetosphere↗
Numerical Investigation of Flow Over Periodic Hill
Hybrid turbulence modeling strategies that blend between large-eddy simulation and Reynolds-averaged Navier-Stokes promise to make optimal use of the available grid resolution. A delayed detached eddy simulation hybrid turbulence modeling strategy, based on the shear-stress transport turbulence model, was implemented into a finite-volume compressible Navier-Stokes code. Then the flow through a two-dimensional periodic hill channel was simulated. This geometry is a widely accepted benchmark test case for validating hybrid turbulence modeling strategies. The computational grid for the simulation was downloaded from a hybrid turbulence model validation website. The present hybrid turbulence model simulation predicts the size of the separated flow region as well as velocity and Reynolds stress profiles in good agreement with large-eddy simulation reference data.
Computational Fluid Dynamics↗