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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

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↗
DOE OSTI2022

Partial Wave Analysis of the omega pi^- Final State Photoproduced at GlueX

The meson has traditionally been understood as the bound state of a quark and an anti-quark, but there is experimental evidence for exotic meson states, which have properties that cannot be produced by this quark-antiquark model. One candidate for such exotic systems is hybrid meson, predicted by lattice QCD, which includes gluonicexcitation in its wavefunction. One of the prominent decay modes of the lightest predicted exotic hybrid is expected to be b1pi. THis dissertation presernts an analysis of the gamma rho -> omega pi- Delta ++ channel produced at the GlueX experiment, in the mass range where the b1(1235) meson is prominent. An amplitude analysis is performed on the decay of the omega pi- system, in order to extract the resonance parameters of the b-1, as well as analyze the resonant and non-resonant background.

Schertz, Amy↗
DOE OSTI2022

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Genetic mapping and prediction of flowering time and plant height in a maize Stiff Stalk MAGIC population

Abstract The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a 6-parent multiparent advanced generation intercross population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in 2 field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to 2 testers, respectively, and evaluated as hybrids in 2 locations in 2018 and 2019 using an incomplete block design. Markers were derived from a practical haplotype graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and 2 sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55–0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49–0.55). Overall, this finding is consistent with the overlapping and nonoverlapping significant quantitative trait loci found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.

Genetics & Heredity↗
DOE OSTI2022

Verifying raytracing/Fokker–Planck lower-hybrid current drive predictions with self-consistent full-wave/Fokker–Planck simulations

Raytracing/Fokker–Planck (FP) simulations used to model lower-hybrid current drive (LHCD) often fail to reproduce experimental results, particularly when LHCD is weakly damped. A proposed reason for this discrepancy is the lack of ‘full-wave’ effects, such as diffraction and interference, in raytracing simulations and the breakdown of the raytracing approximation. Previous studies of LHCD using non-Maxwellian full-wave/FP simulations have been performed, but these simulations were not self-consistent and enforced power conservation between the FP and full-wave code using a numerical rescaling factor. Here, we have created a fully self-consistent full-wave/FP model for LHCD that is automatically power conserving. This was accomplished by coupling an overhauled version of the non-Maxwellian TORLH full-wave solver and the CQL3D FP code using the Integrated Plasma Simulator. We performed converged full-wave/FP simulations of Alcator C-Mod discharges and compared them with raytracing. We found that excellent agreement in the power deposition profiles from raytracing and TORLH could be obtained, however, TORLH had somewhat lower current drive efficiency and broader power deposition profiles in some cases. This discrepancy appears to be a result of numerical limitations present in the TORLH model and a small amount of diffractional broadening of the TORLH wave spectrum. Our results suggest full-wave simulation of LHCD is likely not necessary as diffraction and interference represented only a small correction that could not account for the differences between simulations and experiment.

Physics↗
DOE OSTI2023

Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides

The computational design of suitable multiferroic double perovskite oxides requires finding materials that exhibit sizable polarization, magnetization, and coupling between them. Oxides with the chemical formula of AA'BB'O 6 with building blocks of ABO 3 single perovskite oxides in centrosymmetric Pnma symmetry are strong candidates that have been reported to satisfy such criteria. The system lowers to noncentrosymmetric, polar P2 1 symmetry if A/A' layered and B/B' rocksalt cation orderings are imposed. A detailed compositional search over a variety of chemical spaces followed by evaluating their polarization may lead to the identification of more of these compounds with ferroelectric ordering. The standard density functional theory practices to estimate polarization within the Berry phase formalism require the systems to be perfectly insulating. The number of compounds that can be evaluated using this method is therefore limited. In this work, we introduce a predictive learning strategy based on importance sampling to build a series of machine learning models using results from first-principles simulations to predict polarization and the corresponding switching barrier. The geometry-driven features related to charge states and cationic radii play key roles in predicting the switching barrier with complementary contributions from the key structural mode-based order parameters. These modes become important to draw reasonable predictions of polarization components from machine learning models. In conclusion, our predictive models identify candidates with high polarizations and low switching barriers from a pool of double perovskite oxides, suitable for future investigation for their potential applications in spintronic devices.

36 MATERIALS SCIENCE↗
NASA NTRS1985

A hybrid numerical technique for predicting the aerodynamic and acoustic fields of advanced turboprops

A hybrid numerical procedure is presented for the prediction of the aerodynamic and acoustic performance of advanced turboprops. A hybrid scheme is proposed which in principle leads to a consistent simultaneous prediction of both fields. In the inner flow a finite difference method, the Approximate-Factorization Alternating-Direction-Implicit (ADI) scheme, is used to solve the nonlinear Euler equations. In the outer flow the linearized acoustic equations are solved via a Boundary-Integral Equation (BIE) method. The two solutions are iteratively matched across a fictitious interface in the flow so as to maintain continuity. At convergence the resulting aerodynamic load prediction will automatically satisfy the appropriate free-field boundary conditions at the edge of the finite difference grid, while the acoustic predictions will reflect the back-reaction of the radiated field on the magnitude of the loading source terms, as well as refractive effects in the inner flow. The equations and logic needed to match the two solutions are developed and the computer program implementing the procedure is described. Unfortunately, no converged solutions were obtained, due to unexpectedly large running times. The reasons for this are discussed and several means to alleviate the situation are suggested.

Homicz, G. F.↗
DOE OSTI2023

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

In this work we develop and present a machine learning based downscaling approach using generative adversarial networks (GANs). GANs learn to distinguish the relationships between low-resolution and high-resolution simulations and generate accurate high-resolution output from low-resolution input (Stengel, Glaws, Hettinger, & King, 2020). Low-resolution numerical weather prediction (NWP) simulations at 9-km spatial and 60-minute temporal resolution were executed over Southeast Asia to provide input to the GANs model. GANs for wind, temperature, and pressure were trained on coarsened WIND Toolkit data with a diverse sampling of terrain and meteorological conditions. After training, the NWP simulations over Southeast Asia were enhanced by 3x along each horizontal spatial dimension and 4x along the temporal dimension. This novel downscaling approach generated 15-year high-resolution wind, temperature, and pressure data from January 2007 through December 2021 at multiple hub heights over Southeast Asia at 3-km spatial and 15-minute temporal resolution with a 16x reduction in compute time over standard dynamical downscaling.

17 WIND ENERGY↗
NASA NTRS1990

A hybrid method for prediction of propeller performance

An analytical/computational code has been developed to predict the performance of advanced turboprop propellers. The method of solution is based on strip theory and conservation of linear and angular momenta applied to a control volume enclosing the propeller and its far wake. An iterative procedure is used to account for the induced axial and rotational velocities. Sectional aerodynamic coefficients are explicitly accounted for by thin airfoil theory and refinement of the results is obtained using a two-dimensional vortex panel method and Euler equations. The results obtained compare well with wind-tunnel data obtained for advanced propellers, such as the Hamilton Standard SR-2 and SR-7.

Takallu, M. A.↗
DOE OSTI2020

Simplified Two-Stage Model Predictive Control for a Hybrid Multilevel Converter With Floating H-Bridge

This paper proposes a simplified two-stage model predictive control (ST-MPC) for a hybrid multilevel converter, which is an active-neutral-point-clamped converter with floating H-bridge (ANPC-H). The objective of the first stage is to select the voltage vector that has the optimal current tracking performance by using a novel geometrical positioning approach in the complex plane. The second stage selects the best switching state among all the available switching states that belong to the same voltage vector obtained in the first stage, to balance dc capacitor voltages and reduce the common mode voltage. The proposed ST-MPC can dramatically reduce the computational burden and ensure the best current tracking by the two-stage structure, such that the execution time is much shorter compared with the conventional MPC. In addition, the geometrical positioning approach in the first stage is generic and can be applicable for any multilevel converters with N-level output; thus, this ST-MPC can be applied for both sevenand nine-level operation of the hybrid ANPC-H converter under different dc voltage ratios. Both simulation results and experimental results obtained on a silicon carbide hybrid ANPC-H converter prototype validate the feasibility and effectiveness of the proposed ST-MPC strategy.

42 ENGINEERING↗
DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

A Hybrid CFD/Engineering Model for Predicting Plume Induced Erosion and Cratering

The Descent Interpolated Gas-Granular Erosion Model (DIGGEM) is a hybrid computational fluid dynamics (CFD) / engineering model calibrated with flight data from Apollo. It is used for the prediction of plume induced viscous erosion and cratering during the descent of landing vehicles. The model assumes a functional relationship between local shear stress and eroded mass flux. It is used along with a suite of other tools to predict complex plume induced environments for Human Landing System (HLS) and Commercial Lunar Payload Services (CLPSS) vehicles.

PSI↗
NASA NTRS2015

Model-Based Prognostics of Hybrid Systems

Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

prognosis↗