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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Study of effect of PWR cold leg temperature gradient on reactor core condition

Effects of temperature and flow gradients in Westinghouse designed three-loop Pressurized Water Reactor (PWR) cold legs, the piping between the main coolant pump and the reactor vessel, were evaluated using Computational Fluid Dynamics (CFD) code STAR CCM+ and coupled neutronic and thermal-hydraulic (T/H) code system VERA. In the parametric study, several symmetric and asymmetric temperature gradients that were significantly larger than those observed from plant measurements were applied to the cold leg inlets for comparison with the base case without any temperature gradient. A CFD model using the STAR-CCM+ code was developed for a portion of the RCS region between the Reactor Coolant Pump (RCP) and the core inlet based on previously validated modeling approach. The CFD simulation results were processed for the temperature and flow rate distributions at the core inlet as input to the VERA calculations. The VERA code system consists of COBRA-TF (CTF) for thermal-hydraulics, MPACT for reactor physics and neutron transport, and ORIGEN for isotopic depletion. The VERA model was for depletion calculations of a high-burnup loading pattern with the reactor core in pin-by-pin and subchannel resolution. The results of the study indicate that the postulated temperature gradients within the PWR cold legs do not result in any significant changes in the core inlet temperature distributions and the core power distributions during the reactor operation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

GeN-ROM—An OpenFOAM®-based multiphysics reduced-order modeling framework for the analysis of Molten Salt Reactors

This work presents a projection-based multiphysics Model Order Reduction (MOR) framework for the analysis of nuclear systems and its application to parametric simulations of Molten Salt Reactors (MSR). The framework, named GeN-ROM, is developed using OpenFOAM® and employs a Proper Orthogonal Decomposition aided Reduced-Basis technique (POD-RB). It can be used to reduce steady-state and transient multiphysics problems involving parametric fluid dynamics, heat exchange, and neutronics phenomena. For the treatment of structural elements in the hydraulic systems, a porous medium approach has been adopted. The reduction process is data-driven and snapshot information is extracted via POD to learn the solution manifold and to build global spatial basis functions. At the data collection phase, GeN-ROM makes use of the solvers available in GeN-Foam, a similarly OpenFOAM®-based multiphysics framework developed for the analysis of nuclear reactors. The global bases are used both to approximate the solution fields and to project the full-order equations onto lower-dimensional subspaces, thus considerably reducing the number of unknowns in a numerical system. This reduction leads to significant computational speedups, which is ideal for multi-query applications such as uncertainty quantification or design optimization. The developed tool has been tested using a 2D multiphysics model of the Molten Salt Fast Reactor (MSFR) with steady-state and transient scenarios, with speedups on the order of 10 – 10 5 .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Local modeling for FRF estimation with noisy input measurements

The frequency response function (FRF) is an essential means by which dynamic systems are qualified. In recent years, local modeling approaches have been extensively researched and shown to significantly outperform traditional FRF estimators. However, the standard local modeling approach assumes a perfectly-known system input, which results in biased FRF estimates in the presence of input noise. This paper derives a simple adjustment that can be used to improve FRF estimation for systems subjected to random excitation with noisy input data. This improvement can be implemented with little modification to standard local modeling algorithms and with little additional computational burden. The adjustment is coupled with a model selection procedure to avoid underfitting and overfitting. In conclusion, the methods presented in this paper are validated on a simulation, and they are shown to reduce bias due to input noise.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.

dynamic load modeling↗
DOE OSTI2022

On the hot surface ignition of a wall-stagnating spray flame

The ignition of fuel sprays due to interaction with hot surfaces is an important phenomenon in the safety analysis of many engineering systems. We perform a parametric study of the hot surface ignition (HSI) of a fuel spray approaching a heated surface caused by the accidental leakage of a fuel line. Here, to this end, we employ a one-dimensional Eulerian-Eulerian formulation with a non-equilibrium evaporation model and a realistic chemical mechanism to describe $n$-dodecane fuel chemistry. We first describe and analyze the phenomenology of the unsteady processes leading to ignition using non-dimensionalized quantities. Through consideration of the temporal development of the most reactive mixture, we demonstrate that ignition occurs at a fuel-lean composition in a premixed region near the hot surface. Using non-dimensional parameters identified from the governing equations, we perform a parametric study of the time, location and local mixture composition at ignition and determine the ignition limits. We then identify the most important parametric sensitivities for physical analysis using a data-driven classification method. Our analysis demonstrates a contraction of the ignition limits with increased Stokes number and a regime of parametric insensitivity of igniting mixture composition. We also show that at high Damköhler numbers, the ignition location conforms to the parametric behavior of the thermal boundary layer, whereas at low Damköhler numbers approaching the ignition limit it reaches a near-unity value of the quenching Peclet number. We then compare the demonstrated parametric dependencies to the results of the quasi-steady asymptotic ignition literature, showing that our results are consistent with those obtained analytically within the limitations imposed by the simplified formulation of the latter.

42 ENGINEERING↗
DOE OSTI2022

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bimolecular Peroxy Radical (RO 2 ) Reactions and Their Relevance in Radical Initiated Oxidation of Hydrocarbons

The kinetics of peroxy radical (RO 2 ) reactions have been of long-standing interest in atmospheric and combustion chemistry. Nevertheless, the lack of kinetic studies at higher temperatures for their reactions with other radicals such as OH has precluded the inclusion of this class of reactions in detailed kinetics models developed for combustion applications. In this work, guided by the limited room temperature experimental studies on selected alkyl-peroxy radicals and literature theoretical kinetics on the prototypical CH 3 O 2 + OH system, we have performed parametric studies on the effect of uncertainties in the rate coefficients and branching ratios to potential product channels for RO 2 + OH reactions at higher temperatures. Literature kinetics models were used to simulate autoignition delays, laminar flame speeds, and speciation profiles in flow and stirred reactors for a variety of common combustion-relevant fuels. Inclusion of RO 2 + OH reactions was found to retard autoignition in fuel-lean (φ = 0.5) mixtures of ethane and dimethyl ether in air. The observed effects were noticeably more pronounced in ozone-enriched combustion of ethane and dimethyl ether. The simulations also examined the influence of ozone doping levels, pressures, and equivalence ratios for both ethane and dimethyl ether oxidation. Sensitivity and flux analyses revealed that the RO 2 + OH reaction is a significant sink of RO 2 radicals at the early stage of autoignition, affecting fuel oxidation through RO 2 ↔ QOOH, RO 2 ↔ alkene + HO 2 , or RO 2 + HO 2 ↔ ROOH + O 2 . Additionally, the kinetic stability of the trioxide formed from RO 2 + OH reactions was investigated using master equation analyses. Last, we discuss other bimolecular reactions that are missing in literature kinetics models but are relevant to hydrocarbon oxidation initiated by external radical sources (plasma-enhanced, ozone-enriched combustion, etc.). In conclusion, the present simulations provide a strong motivation for better characterizing the bimolecular kinetics of peroxy radicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Benchmarking the Performance of the ReaxFF Reactive Force Field on Hydrogen Combustion Systems

A thorough understanding of the kinetics and dynamics of combusting mixtures is of considerable interest, especially in regimes beyond the reach of current experimental validation. The ReaxFF reactive force field method has provided a way to simulate large-scale systems of hydrogen combustion via a parametrized potential that can simulate bond breaking. This modeling approach has been applied to hydrogen combustion, as well as myriad other reactive chemical systems. In this work, we benchmark the performance of several common parametrizations of this potential against higher-level quantum mechanical (QM) approaches. We demonstrate instances where these parametrizations of the ReaxFF potential fail both quantitatively and qualitatively to describe reactive events relevant for hydrogen combustion systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multicomponent gas mixture parametric CFD study of condensation heat transfer in small modular reactor system safety

Safety is always the primary concern for designing and analyzing nuclear reactor systems. The requirements for the safety margin for advanced small modular reactor (SMR) systems are targeted even higher than the conventional commercial large-scale nuclear reactors incorporating the passive and inherent safety systems. The SMR systems are designed with the condensation passive containment cooling system (PCCS), which plays a critical role in removing reactor heat during a steam release accident case. However, the presence of non-condensable gas (NCG), like air, reduces the heat transfer performance. This physics phenomenon becomes multifactorial for nuclear reactor containment during a fuel failure accident case that releases hydrogen gas. Besides, the mixture component of steam-air-hydrogen varies in reactor accident cases, which need simulation and validation keeping parameters of importance. Reviews showed that previous studies for SMR’s PCCCS did not cover the condensation heat transfer (CHT) in the presence of multicomponent NCG mixture parametric computational fluid dynamics (CFD) simulation and validation, making a research gap in the SMR design safety. A comprehensive CHT parametric CFD study was performed for SMR PCCS to fill this research gap. This study used experimental data as simulation 3D physics domain inlet and outlet boundary conditions. However, the wall boundary conditions were constant temperature, curve-fit, and annular coolant for verifying the turbulence models. Parametric simulations were performed, verified, and optimized for steam-NCGs mixtures. The multicomponent gases, multiphase mixtures, and fluid film condensation models were applied with associated turbulence models. The results of the parametric study were evaluated for realistic reactor conditions. Results showed that parametric study provided critical insight about the dependency of multicomponent gas mixture parameters that supports reactor safety design, analysis, and licensing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Magnetized laser–plasma interactions in high-energy-density systems: Parallel propagation

In this study, we investigate parametric processes in magnetized plasmas, driven by a large-amplitude pump light wave. Our focus is on laser–plasma interactions relevant to high-energy-density (HED) systems, such as the National Ignition Facility and the Sandia MagLIF concept. We present a self-contained derivation of a “parametric” dispersion relation for magnetized three-wave interactions, meaning the pump wave is included in the equilibrium, similar to the unmagnetized work of Drake et al., Phys. Fluids 17, 778 (1974). For this, we use a multi-species plasma fluid model and Maxwell's equations. The application of an external B field causes right- and left-polarized light waves to propagate with differing phase velocities. This leads to Faraday rotation of the polarization, which can be significant in HED conditions. Phase-matching and linear wave dispersion relations show that Raman and Brillouin scattering have modified spectra due to the background B field, though this effect is usually small in systems of current practical interest. We study a scattering process we call stimulated whistler scattering, where a light wave decays to an electromagnetic whistler wave (ω≲ω ce ) and a Langmuir wave. This only occurs in the presence of an external B field, which is required for the whistler wave to exist.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database

Hygrothermal simulations provide insight into the energy performance and moisture durability of building envelope components under dynamic conditions. The inputs required for hygrothermal simulations are extensive, and carrying out simulations and analyses requires expert knowledge. An expert system, the Building Science Advisor (BSA), has been developed to predict the performance and select the energy-efficient and durable building envelope systems for different climates. The BSA consists of decision rules based on expert opinions and thousands of parametric simulation results for selected wall systems. The number of potential wall systems results in millions, too many to simulate all of them. We present how machine learning can help predict durability data, such as mold growth, while minimizing the number of simulations needed to run. The simulation results are used for training and validation of machine learning tools for predicting wall durability. We tested Artificial Neural Network (ANN) and Gradient Boosted Decision Trees (GBDT) for their applicability and model accuracy. Models developed with both methods showed adequate prediction performance (root mean square error of 0.195 and 0.209, respectively). Finally, we introduce how the information supports guidance for envelope design via an easy-to-use web-based tool that does not require the end-user to run hygrothermal simulations.

Salonvaara, Mikael↗
DOE OSTI2025

Parametric matrix models

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

Computational science↗
DOE OSTI2023

Strong parametric dispersive shifts in a statically decoupled two-qubit cavity QED system

Qubits in cavity quantum electrodynamic (QED) architectures are often operated in the dispersive regime, in which the operating frequency of the cavity depends on the energy state of the qubit, and vice versa. The ability to tune these dispersive shifts provides additional options for performing either quantum measurements or logical manipulations. Here we couple two transmon qubits to a lumped-element cavity through a shared superconducting quantum interference device (SQUID). In this work, our design balances the mutual capacitive and inductive circuit components so that both qubits are statically decoupled from the cavity with low flux sensitivity, offering protection from decoherence processes. Parametric driving of the SQUID flux enables independent, dynamical tuning of each qubit’s interaction with the cavity. As a practical demonstration, we perform pulsed parametric dispersive readout of both qubits. The dispersive frequency shifts of the cavity mode follow the theoretically expected magnitude and sign. This parametric approach creates an extensible, tunable cavity QED framework with various future applications, such as entanglement and error correction via multi-qubit parity readout, state and entanglement stabilization, and parametric logical gates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗