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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling laser interactions with aluminum and tantalum targets using a hybrid atomistic-continuum model

A hybrid atomistic-continuum method can model the microstructure evolution of metals subjected to laser irradiation. This method combines classical molecular dynamics (MD) simulations with the two-temperature model (TTM) to account for the laser energy absorption and heat diffusion behavior. Accurate prediction of the temperature evolution in the combined MD-TTM method requires reliable accuracy in electron heat capacity, electron thermal conductivity, and electron–phonon coupling factor across the temperatures generated. This study uses the electronic density of states (DOS) obtained from first-principle calculations. The calculated electron temperature-dependent parameters are used in MD-TTM simulations to study the laser metal interactions in FCC and BCC metals and the phenomenon of laser shock loading and melting. This study uses FCC Al and BCC Ta as model systems to demonstrate this capability. When subjected to short pulsed laser shocks, the dynamic failure behavior predicted using temperature-dependent parameters is compared with the experimentally reported single-crystal and nanocrystalline Al and Ta systems. The MD-TTM simulations also investigate laser ablation and melting behavior of Ta to compare with the ablation threshold reported experimentally. In conclusion, this manuscript demonstrates that integrating the temperature-dependent parameters into MD-TTM simulations leads to the accurate modeling of the laser–metal interaction and allows the prediction of the kinetics of the solid–liquid interface.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Core-edge modeling of gyrokinetic turbulence by coupling the delta-f and total-f models in the XGC code

Whole device gyrokinetic turbulence is modeled using a delta-f model in the core coupled to a total-f model in the edge. This new model is implemented in the gyrokinetic code X-Point Included Gyrokinetic Code (XGC) and works within a single simulation. It employs a canonical Maxwellian background in the core and the usual local Maxwellian in the edge. These different core and edge backgrounds are coupled over an overlap region. A thermal-bath operator is used to keep δf small in the core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Accurate Compression of Tabulated Chemistry Models with Partition of Unity Networks

Tabulated chemistry models are widely used to simulate large-scale turbulent fires in applications including energy generation and fire safety. Tabulation via piecewise Cartesian interpolation suffers from the curse-of-dimensionality, leading to a prohibitive exponential growth in parameters and memory usage as more dimensions are considered. Artificial neural networks (ANNs) have attracted attention for constructing surrogates for chemistry models due to their ability to perform high-dimensional approximation. However, due to well-known pathologies regarding the realization of suboptimal local minima during training, in practice they do not converge and provide unreliable accuracy. Partition of unity networks (POUnets) are a recently introduced family of ANNs which preserve notions of convergence while performing high-dimensional approximation, discovering a mesh-free partition of space which may be used to perform optimal polynomial approximation. In this work, we assess their performance with respect to accuracy and model complexity in reconstructing unstructured flamelet data representative of nonadiabatic pool fire models. Our results show that POUnets can provide the desirable accuracy of classical spline-based interpolants with the low memory footprint of traditional ANNs while converging faster to significantly lower errors than ANNs. For example, we observe POUnets obtaining target accuracies in two dimensions with 40 to 50 times less memory and roughly double the compression in three dimensions. We also address the practical matter of efficiently training accurate POUnets by studying convergence over key hyperparameters, the impact of partition/basis formulation, and the sensitivity to initialization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modeling Geologic Waste Repository Systems Below Residual Saturation

The heat generated by high-level radioactive waste can pose numerical and physical challenges to subsurface flow and transport simulators if the liquid water content in a region near the waste package approaches residual saturation due to evaporation. Here, residual saturation is the fraction of the pore space occupied by liquid water when the hydraulic connectivity through a porous medium is lost, preventing the flow of liquid water. While conventional capillary pressure models represent residual saturation using asymptotically large values of capillary pressure, here, residual saturation is effectively modeled as a tortuosity effect alone. Treating the residual fluid as primarily dead-end pores and adsorbed films, relative permeability is independent of capillary pressure below residual saturation. To test this approach, PFLOTRAN is then used to simulate thermal-hydrological conditions resulting from direct disposal of a dual-purpose canister in unsaturated alluvium using both conventional asymptotic and revised, smooth models. Importantly, while the two models have comparable results over 100 000 years, the number of flow steps required is reduced by approximately 94%.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A fully coupled thermal–microstructural–mechanical finite element process model for directed energy deposition additive manufacturing of Ti–6Al–4V

A fully coupled thermal–microstructural–mechanical finite element modelling framework is developed to investigate the distortion and residual stresses during directed energy deposition (DED) of multi-phase Ti–6Al–4V alloy. The Johnson–Cook constitutive model is used to predict the yield strength of each phase as a function of strain, strain rate and temperature where the flow stress is calculated by a linear mixing rule based on the volumetric phase fractions. A thin-walled rectangular sample is chosen as the reference geometry and the results are compared with experimentally measured in situ thermal history and distortion data, where a reasonable agreement is achieved. The proposed modelling framework with physics-based material constitutive model provides useful information for a better understanding of process–microstructure–property relations in additive manufacturing by DED.

Tunay, Merve↗
DOE OSTI2022

Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and P l a n c k thermal Sunyaev-Zel’dovich effect observations. II. Modeling and constraints on halo pressure profiles

Hot, ionized gas leaves an imprint on the cosmic microwave background via the thermal Sunyaev-Zel’dovich (tSZ) effect. The cross-correlation of gravitational lensing (which traces the projected mass) with the tSZ effect (which traces the projected gas pressure) is a powerful probe of the thermal state of ionized baryons throughout the Universe and is sensitive to effects such as baryonic feedback. In a companion paper (Gatti et al. Phys. Rev. D 105, 123525 (2022)), we present tomographic measurements and validation tests of the cross-correlation between Galaxy shear measurements from the first three years of observations of the Dark Energy Survey and tSZ measurements from a combination of Atacama Cosmology Telescope and Planck observations. In this work, we use the same measurements to constrain models for the pressure profiles of halos across a wide range of halo mass and redshift. We find evidence for reduced pressure in low-mass halos, consistent with predictions for the effects of feedback from active Galactic nuclei. We infer the hydrostatic mass bias (B ≡ M 500c /M SZ from our measurements, finding B = 1.8 ± 0.1 when adopting the Planck-preferred cosmological parameters. We additionally find that our measurements are consistent with a nonzero redshift evolution of B, with the correct sign and sufficient magnitude to explain the mass bias necessary to reconcile cluster count measurements with the Planck-preferred cosmology. Our analysis introduces a model for the impact of intrinsic alignments (IAs) of galaxy shapes on the shear-tSZ correlation. We show that IA can have a significant impact on these correlations at current noise levels.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . I. Construction of CMB lensing maps and modeling choices

Joint analyses of cross-correlations between measurements of galaxy positions, galaxy lensing, and lensing of the cosmic microwave background (CMB) offer powerful constraints on the large-scale structure of the Universe. In a forthcoming analysis, we will present cosmological constraints from the analysis of such cross-correlations measured using Year 3 data from the Dark Energy Survey (DES), and CMB data from the South Pole Telescope (SPT) and Planck. Here we present two key ingredients of this analysis: (1) an improved CMB lensing map in the SPT-SZ survey footprint and (2) the analysis methodology that will be used to extract cosmological information from the cross-correlation measurements. Relative to previous lensing maps made from the same CMB observations, we have implemented techniques to remove contamination from the thermal Sunyaev Zel’dovich effect, enabling the extraction of cosmological information from smaller angular scales of the cross-correlation measurements than in previous analyses with DES Year 1 data. We describe our model for the cross-correlations between these maps and DES data, and validate our modeling choices to demonstrate the robustness of our analysis. We then forecast the expected cosmological constraints from the galaxy survey-CMB lensing auto and cross-correlations. We find that the galaxy-CMB lensing and galaxy shear-CMB lensing correlations will on their own provide a constraint on S 8 =σ 8 $\sqrt{Ωm/0.3}$ at the few percent level, providing a powerful consistency check for the DES-only constraints. Finally, we explore scenarios where external priors on shear calibration are removed, finding that the joint analysis of CMB lensing cross-correlations can provide constraints on the shear calibration amplitude at the 5% to 10% level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Comparative Study of Nonlinear Black-Box Modeling for Power Electronics Converters

With the increasing penetration level of renewable sources and power electronics loads in modern power systems, accurate and computationally efficient models are needed. Black-box model (BBM) could be a useful method in such systems. However, not very extensive research efforts have been made for power electronics BBM so far, and existing works mostly focus on steady-state operation, neglecting the important transient behaviors such as load transients, voltage transients, and faults. This paper presents a comparative study of three commonly used nonlinear BBM approaches for transient behaviors of power electronics converters. Comparison methods are proposed, and the evaluations are conducted under different transients using a grid-connected single-phase photovoltaic inverter. The findings of this study provide valuable references for further feasibility investigations on implementing BBMs in large-scale power electronics-rich power systems.

Qiao, Liang↗
DOE OSTI2024

Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models

As the power system continues to be flooded with intermittent resources, it becomes more important to accurately assess the role of hydro and its impact on the power grid. While hydropower generation has been studied for decades, dependency of power generation on water availability and constraints in hydro operation are not well represented in power system models used in the planning and operation of large-scale interconnection studies. There are still multiple modeling gaps that need to be addressed; if not, they can lead to inaccurate operation and planning reliability studies, and consequently to unintentional load shedding or even blackouts. As a result, it is very important thathydropower is represented correctly in both steady-state and dynamic power system studies. In this paper, we discuss the development and use of the Hydrological Dispatch and Analysis Tool (Hy-DAT) as an interactive graphical user interface (GUI), that uses a novel methodology to address the hydropower modeling gaps like water availability and inter-dependency using a database, and algorithms, to generate accurate representative models for power system simulation.

Wang, Dewei↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

deployment↗
DOE OSTI2024

Battery Degradation Modeling in Hybrid Power Plants: An Island System Unit Commitment Study: Preprint

As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs.

battery degradation↗
DOE OSTI2023

Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County

Exploring climate-induced impacts on building energy consumption can provide valuable insights for sustainable energy planning and environmental management in the face of a changing climate. By utilizing future weather data statistically downscaled from the Intergovernmental Panel on Climate Change (IPCC) General Circulation Models (GCMs) from 2020-2100, this paper presents a broadening industry-consensus approach for generating future Typical Meteorological Year (fTMY) weather files through a combination of statistical downscaling and high-performance computing that generalizes across decades, multiple locations for a region, and varying climate models. Furthermore, these fTMY files have been generated for 3,128 US counties for capturing potential weather on a 20-year basis.

Building↗
DOE OSTI2023

BSM (Bioenergy Scenario Model) 2023 FKA: Biomass Scenario Model [SWR-09-09]

The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.

Bush, Brian↗
DOE OSTI2024

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract

Biswas, Ankita↗
DOE OSTI2020

Diffusion Models to Construct a First Principles Multipole-Based Cable Braid Model for Conducting Wires in the Time Domain

We describe here diffusion models apt to construct a multipole-based, cable braid time domain model for conducting wires. Implementation details of both a ladder network valid for time-domain signals with all frequency content and an approximate single-stage circuit valid for low-frequency dominated time signals (such as electromagnetic pulses) are reported. This time domain model can be leveraged to treat system-generated electromagnetic pulse events, as well as used to further confirm the correctness of the multipole-based, cable braid frequency domain model.

42 ENGINEERING↗
DOE OSTI2020

Multiscale modeling high-order methods and data-driven modeling

Projection-based reduced-order models (ROMs) comprise a promising set of data-driven approaches for accelerating the simulation of high-fidelity numerical simulations. Standard projection-based ROM approaches, however, suffer from several drawbacks when applied to the complex nonlinear dynamical systems commonly encountered in science and engineering. These limitations include a lack of stability, accuracy, and sharp a posteriori error estimators. This work addresses these limitations by leveraging multiscale modeling, least-squares principles, and machine learning to develop novel reduced-order modeling approaches, along with data-driven a posteriori error estimators, for dynamical systems. Theoretical and numerical results demonstrate that the two ROM approaches developed in this work - namely the windowed least-squares method and the Adjoint Petrov - Galerkin method - yield substantial improvements over state-of-the-art approaches. Additionally, numerical results demonstrate the capability of the a posteriori error models developed in this work.

97 MATHEMATICS AND COMPUTING↗