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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗
DOE OSTI2025

Magneto-opto-phononic inverse Faraday effect

Nonlinear frequency conversion processes, such as optical rectification, difference-frequency generation, and sum-frequency generation, are fundamental for producing electromagnetic radiation at diverse frequencies. In this work, we demonstrate that coherently excited infrared-active phonons can act as transducers for generating nonlinear magnetizations through phonon-magnon interactions, analogous to nonlinear optical frequency conversion. We derive analytical solutions for the time-dependent magnetizations arising from the second-order response to the electric field component of an ultrashort laser pulse. These solutions enable us to define second-order nonlinear magneto-electric susceptibilities, which describe rectification, impulsive excitation, and sum-frequency excitation of coherent magnons. Our theoretical framework naturally incorporates the conventional magneto-optic and phonon inverse Faraday effects and predicts a hybrid magneto-opto-phononic inverse Faraday effect involving photon-phonon-magnon scattering. This work highlights nonlinear phononics as a pathway for controlling magnetization in solids.

Landau-Lifschitz-Gilbert equation↗
NASA NTRS1975

Super-hybrid composites - An emerging structural material

Specimens of super-hybrids and advanced fiber composites were subjected to extensive tests to determine their mechanical properties, including impact and thermal fatigue. The super-hybrids were fabricated by a procedure similar to that reported by Chamis et al., (1975). Super-hybrids subjected to 1000 cycles of thermal fatigue from -100 to 300 F retained over 90% of their longitudinal flexural strength and over 75% of their transverse flexural strength; their transverse flexural strength may be as high as 8 times that of a commercially supplied boron/1100-Al composite. The thin specimen Izod longitudinal impact resistance of the super-hybrids was twice that of the boron/110-Al material. Super-hybrids subjected to transverse tensile loads exhibited nonlinear stress-strain relationships. The experimentally determined initial membrane (in-plane) and bending elastic properties of super-hybrids were predicted adequately by linear laminate analysis.

Chamis, C. C.↗
NASA NTRS1995

Studies with Cluster upstream and downstream of the bow shock: An experimenter's perspective

Some open questions in the physics of bow shock formation, the evolution of the particle distributions from solar wind into the magnetosheath, and the acceleration of ions at the moment of the shock are summarized. A layout of the current situation is presented in view of recent theoretical developments and the new diagnostic tools provided by the Cluster mission. The transition of ions across the quasi-perpendicular bow shock and their downstream thermalization are discussed. The processes and spatial scales are found to be species dependent and are discussed for H(+), He(2+), and He(+). The theory of particle acceleration at quasi-parallel shocks are reviewed. It is shown how Cluster can study the time variable structures of the shock as predicted by hybrid simulation. It is emphasized that high time resolution measurement with simultaneous species separation is necessary for the study of the ion acceleration. Suggestions for the spacecraft separations at the bow shock are suggested.

Moebius, E.↗
NASA NTRS2012

Dipole Alignment in Rotating MHD Turbulence

We present numerical results from long-term CPU and GPU simulations of rotating, homogeneous, magnetohydrodynamic (MHD) turbulence, and discuss their connection to the spherically bounded case. We compare our numerical results with a statistical theory of geodynamo action that has evolved from the absolute equilibrium ensemble theory of ideal MHD turbulence, which is based on the ideal MHD invariants are energy, cross helicity and magnetic helicity. However, for rotating MHD turbulence, the cross helicity is no longer an exact invariant, although rms cross helicity becomes quasistationary during an ideal MHD simulation. This and the anisotropy imposed by rotation suggests an ansatz in which an effective, nonzero value of cross helicity is assigned to axisymmetric modes and zero cross helicity to non-axisymmetric modes. This hybrid statistics predicts a large-scale quasistationary magnetic field due to broken ergodicity , as well as dipole vector alignment with the rotation axis, both of which are observed numerically. We find that only a relatively small value of effective cross helicity leads to the prediction of a dipole moment vector that is closely aligned (less than 10 degrees) with the rotation axis. We also discuss the effect of initial conditions, dissipation and grid size on the numerical simulations and statistical theory.

Shebalin, John V.↗
DOE OSTI2023

Preliminary Monte Carlo and Thermal Hydraulic Analysis using a Hybrid ETF-Corrected-Diffusion Prediction Block

This paper builds upon previous work to accelerate the Picard iteration (PI) method typically applied for coupled Monte Carlo-Thermal hydraulic (MC-TH) solutions. Previously, the use of the generalized transfer functions (GTFs) to predict variation in macroscopic cross sections following a perturbation in TH properties was demonstrated for a subset of simple 3D problems. In addition, the reduced-order transport prediction block relied on the first order perturbation (FOP) method, which was shown to have computational overheads. Recent work replaced the FOP block with a 1-group nodal diffusion solver to eliminate these overheads. While the use of diffusion is desirable for large-scale problems, the new solver introduces significant homogenization error. This work aims to address this issue by using the Jacobian-Free Newton Krylov (JFNK) method to generate a set of super homogenization (SPH) factors to improve the accuracy of the diffusion solution. The SPH factors will be used in conjunction with an improved cross section prediction method – the expanded transfer function (ETF) method – to produce a highly accurate flux prediction for an axial 1D boiling water reactor (BWR) pincell following a large perturbation in moderator density. The ETF-corrected diffusion (ETF-CD) block is shown to be highly accurate for the 1D test case. Future work will investigate the accuracy of the method for a realistic 3D pressurized water reactor core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Time‐Dependent Density Functional Theory of Narrow Band Gap Semiconductors Using a Screened Range‐Separated Hybrid Functional

Abstract Predicting the band structure and optical absorption spectra of narrow band gap semiconductors is challenging for electronic structure methods. Here, it is shown shown that density functional theory can yield accurate band structures and time‐dependent density functional theory (TDDFT) can yield accurate optical absorption spectra for these systems. This is achieved by using a screened range‐separated hybrid (SRSH) functional with a single empirical parameter, fit to reproduce the experimental band gap. By comparing TDDFT results based on the SRSH approach with those obtained based on the Heyd–Scuseria–Ernzerhof functional it is shown that screened long‐range exact exchange improves the accuracy of the TDDFT spectra for these systems.

Wing, Dahvyd↗
NASA NTRS1993

Pdf prediction of supersonic hydrogen flames

A hybrid method for the prediction of supersonic turbulent flows with combustion is developed consisting of a second order closure for the velocity field and a multi-scalar pdf method for the local thermodynamic state. It is shown that for non-premixed flames and chemical equilibrium mixture fraction, the logarithm of the (dimensionless) density, internal energy per unit mass and the divergence of the velocity have several advantages over other sets of scalars. The closure model is applied to a supersonic non-premixed flame burning hydrogen with air supplied by a supersonic coflow and the results are compared with a limited set of experimental data.

Eifler, P.↗
NASA NTRS2019

Predictions of Slat Noise from the 30P30N at High Angles of Attack Using Zonal Hybrid RANS-LES

Aeroacoustic predictions of slat noise from the 30P30N three-element high-lift system at high angles of attack are presented using a zonal hybrid RANS-LES method. The simulations are part of the 5th AIAA Benchmark problems for Airframe Noise Computations (BANC-V) Workshop. An economical approach utilizing structured overset grids with spatially varying span-wise grid resolution and a high-order accurate finite difference method is described. The method is utilized for near-field predictions at three angles of attack: α = 5.5, 9.5, and 14.0 degrees. Far-field noise is obtained by propagating the near-field solution using a permeable surface Ffowcs Williams-Hawkings (FWH) method. Good agreement is obtained with both near-field and far-field Power Spectral Density (PSD) data from an experimental study of the 30P30N in the 2m x 2m Kevlar-wall wind tunnel at the Japan Aerospace Exploration Agency (JAXA). Specifically, the reduction in narrow band peaks and overall broadband noise levels with increasing angle of attack is captured well using the zonal hybrid RANS-LES method.

Housman, Jeffrey A.↗
DOE OSTI2023

Methods and systems for coordinating predictive cruise control, engine-off coasting, and hybrid power split

The enclosed disclosure relates to hybrid vehicles and systems with an engine, a drivetrain with a clutch and a transmission, an electric machine, and a controller. The controller receives lookahead information within a lookahead window and present state information of the hybrid vehicle. The controller determines a predicted coasting opportunity exceeding a predetermined threshold within the lookahead window and determines a cruise control reference speed, a power split between the engine and the electric machine, and a timing of enabling engine-off coasting during the coasting opportunity. The controller deactivates the engine and disengages the clutch at a start of the coasting opportunity when the engine-off coasting is enabled.

Gupta, Rohinish↗
NASA NTRS1971

Predicted characteristics of an optimized series-hybrid conical hydrostatic ball bearing

Optimized series-hybrid fluid-film ball bearings are described and operating characteristics are calculated and discussed. It is predicted that a series-hybrid bearing may be constructed which will reduce ball-bearing speed by 30 percent thereby increasing bearing fatigue life by factors of up to 5.9. Flow rates required are less than 9 kilograms per minute.

Nypan, L. J.↗
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 OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

3P Program: Phenotyping X Prediction = Productivity (Final Scientific/Technical Report)

The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

A Hybrid Approach To Tandem Cylinder Noise

Aeolian tone generation from tandem cylinders is predicted using a hybrid approach. A standard computational fluid dynamics (CFD) code is used to compute the unsteady flow around the cylinders, and the acoustics are calculated using the acoustic analogy. The CFD code is nominally second order in space and time and includes several turbulence models, but the SST k - omega model is used for most of the calculations. Significant variation is observed between laminar and turbulent cases, and with changes in the turbulence model. A two-dimensional implementation of the Ffowcs Williams-Hawkings (FW-H) equation is used to predict the far-field noise.

Lockard, David P.↗
DOE OSTI2021

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗