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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development and Demonstration of a Class 6 Range-Extended Electric Vehicle for Commercial Pickup and Delivery Operation: Preprint

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing representative workday drive cycles for Class 6 operation and an adapted cycle to enable vehicle track testing. A novel, automated driving system was utilized by Southwest Research Institute (SwRI) to improve the repeatability of the track testing conducted to quantify vehicle energy consumption. Cummins used a drivetrain model to design the hybrid control system for increased fuel savings. The control system functionality and fuel savings objective of 50% or more were confirmed by track testing described in this paper.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

Kusmec, Aaron (ORCID:000000032295385X)↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2021

Rashba exciton in a 2D perovskite quantum dot

The Rashba effect has been proposed to give rise to a bright exciton ground state in halide perovskite nanocrystals (NCs), resulting in very fast radiative recombination at room temperature and extremely fast radiative recombination at low temperature. In this paper we find the dispersion of the “Rashba exciton”, i.e., the exciton whose bulk dispersion reflects large spin–orbit Rashba terms in the conduction and valence bands and thus has minima at non-zero quasi-momenta. Placing Rashba excitonsin quasi-2D cylindrical quantum dots, we calculate size-dependent levels of confined excitons and their oscillator transition strengths. Here, we consider the implications of this model for two-dimensional hybrid organic–inorganic perovskites, discuss generalizations of this model to 3D NCs, and establish criteria under which a bright ground exciton state could be realized.

36 MATERIALS SCIENCE↗
DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗
DOE OSTI2021

Physically regularized machine learning emulators of aerosol activation

Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their interactions. As such, various parameterizations have been developed to approximate these details at reduced computational cost and accuracy. Here, we explore how machine learning emulators can be used to bridge this gap in computational cost and parameterization accuracy. We evaluate a set of emulators of a detailed cloud parcel model using physically regularized machine learning regression techniques. We find that the emulators can reproduce the parcel model at higher accuracy than many existing parameterizations. Furthermore, physical regularization tends to improve emulator accuracy, most significantly when emulating very low activation fractions. This work demonstrates the value of physical constraints in machine learning model development and enables the implementation of improved hybrid physical and machine learning models of aerosol activation into next-generation Earth system models.

58 GEOSCIENCES↗
DOE OSTI2022

Development of a simplified calibrated building simulation model of a supermarket for proposed ECMs and control strategies impact evaluation

Calibrated building energy simulation is an important pathway to more energy-efficient buildings, but the information requirements of some approaches to this problem are significant. This is particularly true for supermarkets and other so-called “big-box” retail stores. Another characteristic of supermarkets is the significant interaction between Heating Ventilating and Air Conditioning (HVAC) and refrigeration systems in these buildings. These buildings could contain a wide variety of systems and a degree of load diversity that makes calibrated modeling a challenge. This paper describes a simplified approach that uses OpenStudio and EnergyPlus to combine known building parameters with “typical” parameters, resulting in a simplified building that is amenable to calibration. This approach was applied to a big-box store located in Nashville, Tennessee, and a calibrated model was obtained that was used to study potential energy conservation measures. Further, the paper also explores the capabilities of whole-building energy modeling tools, such as EnergyPlus, for modeling the HVAC controls and sequences and their impact evaluation. Although some measures are precluded by the model simplicity, several measures were found to improve the efficiency of the model and demonstrate that the simplified modeling approach is effective. Practical Application: This paper introduces a hybrid approach of building energy model calibration using limited information available from the actual building in combination with characteristics of a “typical” building of the same type. This hybrid approach would also be applicable for other building types than discussed in this paper to calibrate the building energy model using limited information from the actual building.

42 ENGINEERING↗
DOE OSTI2025

The accuracy of multi-group models for nonlocal electron transport in magnetized plasmas

In the extreme conditions of inertial confinement fusion experiments, heat flow plays a vital role, but local diffusive models frequently break down and overestimate the heat flow. The situation becomes more complicated again in the significant magnetic fields generated during laser–plasma interactions or in magnetized fusion schemes. Accurate non-local and magnetized heat flow computations can be carried out using Vlasov–Fokker–Planck (VFP) simulations, but these are computationally expensive. There is, therefore, significant interest in using faster multi-group models to accurately calculate the non-local heat flow in magnetized plasmas. We benchmark two such multi-group models for calculating the heat flow, M1 and hybrid-AWBS-BGK, against diffusive models and full VFP simulations, before applying the models to realistic example test cases, both magnetized and unmagnetized. We find that the multi-group models generally perform very well for moderate non-localities up to kλmfp∼0.01, but the computational cost increases dramatically. hybrid-AWBS-BGK performs more effectively than M1 at high non-localities, up to kλmfp∼1, due to its adaptive solver and robust P1 closure, but tends to fail in very strong magnetic fields. Both codes are much faster than VFP simulations but are still slow in steep temperature gradients.

Arran, C. (ORCID:0000000286448118)↗
DOE OSTI2025

Nonequilibrium effects in high-gain inertial confinement fusion

Recent experimental demonstrations of ignition and target gain in inertial confinement fusion (ICF) have stimulated interest in exploring the fundamental physics of violent deuterium-tritium (DT) burn in high-gain ICF targets. A significant DT-burn fraction is a necessary condition for high energy gain and large neutron yields (>100MJ). Using classical molecular-dynamics (MD) simulations and a hybrid fluid-kinetic model, we examine how a large fraction of low-energy 𝛼 particles can kick D and T ions out of equilibrium in high-gain ICF targets. The MD results suggest that (1) temperatures of 𝑇 𝐷 and 𝑇 𝑇 can differ by as much as ∼20% of their mean temperature and (2) the deviation of the DT energy distribution from the Maxwell-Boltzmann function can exceed ∼30%. Some of these MD observations, such as the preferential heating of D ions by low-energy 𝛼 particles and the temperature separation, can be explained by a proposed hybrid fluid-kinetic model. Furthermore, the implication of such nonequilibrium effects on the DT reactivity is also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

WEST L-mode record long pulses guided by predictions using Integrated Modeling

A new record was set on the WEST Tokamak, designed to operate long duration plasmas in a tungsten (W) environment, with an injected energy of 1.15 GJ and a plasma duration 364s. Scenario development was supported by integrated modeling using the High Fidelity Plasma Simulator (HFPS), the European IMAS-coupled version of JETTO/JINTRAC, which integrates physics-driven modules into a unified framework. In particular, a reduced model for Lower-Hybrid heating and Current-Drive (LHCD) and the quasi-linear turbulent transport model TGLF are crucial for long pulses predictions up to the Last Closed Flux Surface (LCFS). Using this workflow, a 100 s reference discharge was modeled and plasma kinetic profiles and loop voltage were quantitatively well matched. In preparation for the recent long duration experiments, non-inductive current-drive actuators (I P , n e , P LHCD ) were varied to determine the operational domain going towards fully non-inductive discharges. In particular, decreasing the plasma current is shown to ease the access to such conditions, with a careful monitoring of (n e , P LHCD ) to avoid machine limitations. In addition, post-prediction experiments conducted within the investigated parameter range validated the predicted dependencies and were shown to be in quantitative agreement. Exploratory work on the use of ECCD for MHD stability purpose is also introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

E-Area Low-Level Waste Facility GoldSim System Model

This report documents the development of the E-Area Low-Level Waste Facility (ELLWF) trench system model. The GoldSim® Monte Carlo simulation software is utilized to model the release and transport of radiological inventory disposed (both currently and in the future) within Engineered and Slit Trenches. This model is in support of the sensitivity and uncertainty analysis for the ELLWF Performance Assessment. The ELLWF system model utilizes a hybrid-approach to accurately describe the disposal system. The Hydrologic Evaluation of Landfill Performance model provides the infiltration data to both PORFLOW and GoldSim. PORFLOW is used to calibrate the GoldSim model to ensure confidence in the stochastic results. Finally, the concentrations from GoldSim transport simulations are fed into the SRNL Dose Toolkit to calculate dose impacts and assess plume interaction

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1990

Electrostatic lower hybrid waves excited by electromagnetic whistler mode waves scattering from planar magnetic-field-aligned plasma density irregularities

This paper presents a theoretical model for electrostatic lower hybrid waves excited by electromagnetic whistler mode waves propagating in regions of the magnetosphere and the topside ionosphere, where small-scale magnetic-field-aligned plasma density irregularities are thought to exist. In this model, the electrostatic waves are excited by linear mode coupling as the incident electromagnetic whistler mode waves scatter from the magnetic-field-aligned plasma density irregularities. Results indicate that high-amplitude short-wavelength (5 to 100 m) quasi-electrostatic whistler mode waves can be excited when electromagnetic whistler mode waves scatter from small-scale planar magnetic-field-aligned plasma density irregularities in the topside ionosphere and magnetosphere.

Bell, T. F.↗
DOE OSTI2022

Hybrid RANS-LES of the Atmospheric Boundary Layer for Wind Farm Simulations: Preprint

Wind farm simulations often do not accurately represent wake-atmospheric boundary layer (ABL) interactions, blade boundary layer (BL) dynamics, and turbine-turbine interactions. In this work, we use Active Model Split (AMS), a new hybrid Reynolds-Averaged Navier Stokes (RANS)-large eddy simulation (LES) model, which is well suited to capture these effects because the model can (i) accurately simulate the ABL with the Coriolis effect, (ii) is accurate in adverse pressure gradients such as those near wind turbine blades, and (iii) has sufficiently low computational cost to simulate multiple turbines while resolving the blade BL. For simplicity and consistency we develop AMS to be used throughout the domain rather than in a zonal method. We implement our work in the massively parallel flow solver, Nalu-Wind, so that our model can access the compute resources needed for blade-resolved simulations of multiple wind turbines. To accomplish these aims, we modify the baseline AMS by changing the RANS contribution to SST k - omega with a length scale limiter, adding the Coriolis effect, and developing an appropriate wall treatment. We show that AMS of the ABL with the Coriolis effect matches LES reference results better than those obtained with RANS. We describe our plans to add buoyancy effects and wind turbines to our AMS simulations.

atmospheric boundary layer↗
DOE OSTI2024

Hybrid Solar Thermal Energy System for District Heating Application

Solar district heating (SDH) systems can be good alternatives to conventional systems when they are optimized with hybrid configurations and thermal energy storage (TES). In this scope, a hybrid renewable thermal energy system (RTES) model has been built combining flat plate collector (FPC) solar system with parabolic trough collector (PTC) system via a heat exchanger and coupled with TES. To undertake the hybridization of the system, System Advisor Model (SAM) software was modified, which allowed control over configurations and more accurate modelling of heat transfer between the collectors. The model is first compared to an existing hybrid solar district heating systems (DHS) system in Taars, Denmark. The results showed a good correlation with an overestimation of only 6.4% compared to most recent heat output. Then the same system configuration was modeled in different geographic locations to investigate the impact of changes in direct normal irradiance (DNI) to the heat sink thermal output of the hybrid system. The results showed that the annual net thermal power output in California, USA can be three times more than the annual net thermal power output in Taars, Denmark. Finally, multiple hybrid configurations with varying solar field sizes were simulated based on the heat demand of two different university campuses DHS. The results showed that, retrofit applications of this hybrid DHS system coupled with TES could reduce the natural gas consumption of the existing systems between 25% and 41%. The use of hybrid RTES highlighted in this paper can be extended to many more opportunities.

district heating↗
DOE OSTI2024

MARS-F/K modeling of plasma response and fast ion losses due to RMP in KSTAR

Abstract The toroidal single-fluid magnetohydrodynamic (MHD) code MARS-F (Liu et al 2000 Phys. Plasmas 7 3681) and the MHD-kinetic hybrid code MARS-K (Liu et al 2008 Phys. Plasmas 15 112503) are utilized to study the plasma response to the n = 1 ( n is the toroidal mode number) resonant magnetic perturbation (RMP), applied to suppress the type-I edge localized mode (ELM) in a KSTAR discharge. Both the resistive-rotating and ideal-static plasma models identify strong screening of the resonant radial field harmonics of the applied RMP due to the plasma response, and predict a strong edge-peeling response of the plasma which is consistent with the optimal ELM control coil current configuration adopted in experiment. The RMP-induced radial displacement of the plasma, computed by the resistive-rotating plasma model, agrees reasonably well with that reconstructed from the measured data in the plasma core. Taking into account the drift kinetic response of fast ions, MARS-K hybrid modeling also finds quantitative agreement of the plasma core fluid pressure perturbation with experiment. Based on the MARS-F computed plasma response, a guiding-center orbit-tracing simulation finds about 0.3% of fast ion losses due the n = 1 RMP in the KSTAR ELM control experiment considered. Most losses are associated with counter-current fast ions located near the plasma edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A hybrid-kinetic simulation tool for non-thermal warm x-ray z-pinch sources, with gas-puff and wire array exemplars

Increasing the fluence of z-pinch x-ray radiation sources above ∼ 10 keV has been a long-standing goal for scientists at Sandia National Laboratories’ Z Machine. Optimizing sources for non-thermal “cold Kα” emission in higher atomic-number materials appears to be a promising path to increase warm x-ray yield. However, this emission is generated by supra-thermal electrons, which are not treated in the magnetohydrodynamic (MHD) codes that are typically used in z-pinch source development. MHD codes do not allow for charge separation or space-charge-generated electric fields, and constrain particle kinematics to Maxwellian distributions. The kinetic codes which do accommodate discrete, non-thermal energy distributions are computationally prohibitive when modeling plasmas near solid density and when modeling/tracking higher ionization states. Thus, modeling non-thermal z-pinch sources requires a new simulation tool. In this report, we present a new hybrid modeling capability that uses the fast features of MHD-type particles to the greatest extent possible, then transitions to the slower but more complete kinetic particle treatment to correctly capture the particle energy spectra that generate non-thermal emission. This capability is founded on the fully-relativistic particle-in-cell code Chicago, which already includes fluid particle treatments. The governing equations and hybrid methodology presented here are applied in simulations of an argon gas-puff and a molybdenum wire-array to provide preliminary code validation. The argon simulation is compared to measured implosion times and yields from Jones et al., Phys. Plasmas 22, 020706 (2015). The simulated x-ray yield is within 25% of measurements and the implosion times agree within a few percent. The molybdenum wire array simulation captures the implosion timing reported in Hansen et al., Phys. Plasmas 21, 031202 (2014), but work is needed to verify the available EOS table. These exemplar simulations represents the type of non-thermal sources that will be developed using the hybrid code capability going forward.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗