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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Irradiation of ultrasonic sensors and adhesive couplants for application in light water reactor primary loop piping and components

The Electric Power Research Institute (EPRI) Nuclear Sector and US Department of Energy Light Water Reactor Sustainability Program are committed to engaging in research and development endeavors to address materials aging issues specific to long term operation of light water power reactors. To this effect, EPRI launched an industry initiative to develop nondestructive evaluation systems for online monitoring of existing cracks in light water reactor primary coolant loop piping and components. One of the goals of this initiative is to develop a sensor system (or systems) that can determine nondestructively if cracks are growing or arrested and, in the case of the former, to characterize their growth rates. A missing component of this initiative is an experimental assessment of how sensors and adhesive couplants will perform in service when exposed to chronic energetic neutron radiation, particularly at the primary coolant loop hot and cold leg dissimilar metal welds, which join the primary loop piping to the reactor pressure vessel and reside in the vicinity of the reactor core. The objective of this experimental study was to determine how ultrasonic transducers and adhesive couplants perform when exposed to irradiation in a test reactor to simulate and accelerate in-service exposure. Further, to achieve this objective, the signal stability of piezoelectric transducers and performance of adhesive couplants as a function of accumulated fast neutron fluence were characterized by collecting ultrasonic data in-situ during irradiation. Of particular interest were the ultrasonic signal quality and time decay of the amplitude of acoustic reflections as a function of fast neutron fluence. The results of the study showed that, of the 8 transducer/substrate sample assemblies tested, only 3 generated usable ultrasonic signals through the conclusion of the irradiation campaign. It was found that high temperature epoxy tends to ultrasonically couple the sensors to the substrates better than three types of refractory ceramic cements studied, as is supported by post irradiation examination. The results obtained through this experimental study will be utilized in the achievement of the overall goal of development of a sensor system to perform online monitoring of primary loop components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

SMASH – A new hadronic transport approach

Microscopic transport approaches are the tool to describe the non-equilibrium evolution in low energy collisions as well as in the late dilute stages of high-energy collisions. Here, a newly developed hadronic transport approach, SMASH (Simulating Many Accelerated Strongly-interacting Hadrons) is introduced. The overall bulk dynamics in low energy heavy ion collisions is shown including the excitation function of elliptic flow employing several equations of state. The implications of this new approach for dilepton production are discussed and preliminary results for afterburner calculations at the highest RHIC energy are presented and compared to previous UrQMD results. A detailed understanding of a hadron gas with vacuum properties is required to establish the baseline for the exploration of the transition to the quark-gluon plasma in heavy ion collisions at high net baryon densities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Application of Wavelet-Based Methods for Accelerating Multi-Time-Scale Simulation of Bistable Heterogeneous Catalysis

Here, we report results from a numerical study of multi-time-scale bistable dynamics for CO oxidation on a catalytic surface in a flowing, well-mixed gas stream. The problem is posed in terms of surface and gas-phase submodels that dynamically interact in the presence of stochastic perturbations, reflecting the impact of molecular-scale fluctuations on the surface and turbulence in the gas. Wavelet-based methods are used to encode and characterize the temporal dynamics produced by each submodel and detect the onset of sudden state shifts (bifurcations) caused by nonlinear kinetics. When impending state shifts are detected, a more accurate but computationally expensive integration scheme can be used. This appears to make it possible, at least in some cases, to decrease the net computational burden associated with simulating multi-time-scale, nonlinear reacting systems by limiting the amount of time in which the more expensive integration schemes are required. Critical to achieving this is being able to detect unstable temporal transitions such as the bistable shifts in the example problem considered here. Lastly, our results indicate that a unique wavelet-based algorithm based on the Lipschitz exponent is capable of making such detections, even under noisy conditions, and may find applications in critical transition detection problems beyond catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

Understanding Hydrogenation Chemistry at MgB 2 Reactive Edges from Ab Initio Molecular Dynamics

Solid-state hydrogen storage materials often operate via transient, multistep chemical reactions at complex interfaces that are difficult to capture. Here, we use direct ab initio molecular dynamics simulations at accelerated temperatures and hydrogen pressures to probe the hydrogenation chemistry of the candidate material MgB 2 without a priori assumption of reaction pathways. Focusing on highly reactive ($10\overline{1}0$) edge planes where initial hydrogen attack is likely to occur, we track mechanistic steps toward the formation of hydrogen-saturated BH 4 – units and key chemical intermediates, involving H 2 dissociation, generation of functionalities and molecular complexes containing BH 2 and BH 3 motifs, and B–B bond breaking. Further, the genesis of higher-order boron clustering is also observed. Different charge states and chemical environments at the B-rich and Mg-rich edge planes are found to produce different chemical pathways and preferred speciation, with implications for overall hydrogenation kinetics. The reaction processes rely on B–H bond polarization and fluctuations between ionic and covalent character, which are critically enabled by the presence of Mg 2+ cations in the nearby interphase region. Our results provide guidance for devising kinetic improvement strategies for MgB 2 -based hydrogen storage materials, while also providing a template for exploring chemical pathways in other solid-state energy storage reactions.

08 HYDROGEN↗
DOE OSTI2017

A numerical analysis of a magnetocaloric refrigerator with a 16-layer regenerator

A numerical analysis was conducted to study a room temperature magnetocaloric refrigerator with a 16-layer parallel plates active magnetic regenerator (AMR). Sixteen layers of LaFeMnSiH having different Curie temperatures were employed as magnetocaloric material (MCM) in the regenerator. Measured properties data was used. A transient one dimensional (1D) model was employed, in which a unique numerical method was developed to significantly accelerate the simulation speed of the multi-layer AMR system. As a result, the computation speed of a multi-layer AMR case was very close to the single-layer configuration. The performance of the 16-layer AMR system in different frequencies and utilizations has been investigated using this model. To optimize the layer length distribution of the 16-layer MCMs in the regenerator, a set of 137 simulations with different MCM distributions based on the Design of Experiments (DoE) method was conducted and the results were analyzed. The results show that the 16-layer AMR system can operate up to 84% of Carnot cycle COP at a temperature span of 41 K, which cannot be obtained using an AMR with fewer layers. Here, the DoE results indicate that for a 16-layer AMR system, the uniform distribution is very close to the optimized design.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Investigation of ionization-induced electron injection in a wakefield driven by laser inside a gas cell

Ionization-induced electron injection was investigated experimentally by focusing a driving laser pulse with a maximum normalized potential of 1.2 at different positions along the plasma density profile inside a gas cell, filled with a gas mixture composed of 99%H 2 +1%N 2 . Changing the laser focus position relative to the gas cell entrance controls the accelerated electron bunch properties, such as the spectrum width, maximum energy, and accelerated charge. Simulations performed using the 3D particle-in-cell code WARP with a realistic density profile give results that are in good agreement with the experimental ones. Lastly, we discuss the interest of this regime for optimizing the bunch charge in a selected energy window.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Ion acceleration in electrostatic collisionless shock: on the optimal density profile for quasi-monoenergetic beams

Here, a numerical study on ion acceleration in electrostatic shock waves is presented, with the aim of determining the best plasma configuration to achieve quasi-monoenergetic ion beams in laser-driven systems. It was recently shown that tailored near-critical density plasmas characterized by a long-scale decreasing rear density profile lead to beams with low energy spread (Fiúza et al 2012 Phys. Rev. Lett. 109 215001). In this work, a detailed parameter scan investigating different plasma scale lengths is carried out. As result, the optimal plasma spatial scale length that allows for minimizing the energy spread while ensuring a significant reflection of ions by the shock is identified. Furthermore, a new configuration where the required profile has been obtained by coupling micro layers of different densities is proposed. Lastly, results show that this new engineered approach is a valid alternative, guaranteeing a low energy spread with a higher level of controllability.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Influence of the neutron-skin effect on nuclear isobar collisions at energies available at the BNL Relativistic Heavy Ion Collider

The unambiguous observation of a Chiral Magnetic Effect (CME)-driven charge separation is the core aim of the isobar program at RHIC consisting of $96\atop{40}$Zr+$96\atop{40}$Zr and $96\atop{44}$Ru+$96\atop{44}$Ru collisions at √s NN = 200 GeV. We quantify the role of the spatial distributions of the nucleons in the isobars on both eccentricity and magnetic field strength within a relativistic hadronic transport approach (SMASH, Simulating Many Accelerated Strongly-interacting Hadrons). In particular, we introduce isospin-dependent nucleon-nucleon spatial correlations in the geometric description of both nuclei, deformation for $96\atop{44}$Ru and the so-called neutron skin effect for the neutron-rich isobar i.e. $96\atop{40}$Zr. The main result of this study is a reduction of the magnetic field strength difference between $96\atop{44}$Ru+$96\atop{44}$Ru and $96\atop{40}$Zr+$96\atop{40}$Zr by a factor of 2, from 10% to 5% in peripheral collisions when the neutron-skin effect is included. Further, we find an increase of the eccentricity ratio between the isobars by up to 10% in ultra-central collisions as due to the deformation of $96\atop{44}$Ru while neither the neutron skin effect nor the nucleon-nucleon correlations result into a significant modification of this observable with respect to the traditional Woods-Saxon modeling. Our results suggest a significantly smaller CME signal to background ratio for the experimental charge separation measurement in peripheral collisions with the isobar systems than previously expected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Strangeness production via resonances in heavy-ion collisions at energies available at the GSI Schwerionensynchrotron

Production of strange hadrons in elementary and heavy-ion reactions is studied with the hadronic transport approach called simulating many accelerated strongly interacting hadrons. The poorly known branching ratios of the relevant hadronic resonances are constrained from the known elementary hadronic cross sections and from invariant mass spectra of dileptons. The constrained model is employed as a baseline to compare to heavy-ion-collision experiments at low energies [E kin = (1-2) A GeV] and to predict some of the upcoming pion-beam results at the High-Acceptance Di-Electron Spectrometer, which are expected to be sensitive to the resonance properties. The employed vacuum-resonance approach proves to be viable for small systems at these energies, but for large systems additional in-medium effects might be required.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Gravitational waves from asymmetric oscillon dynamics?

It has been recently suggested that oscillons produced in the early universe from certain asymmetric potentials continue to emit gravitational waves for a number of e-folds of expansion after their formation, leading to potentially detectable gravitational wave signals. We revisit this claim by conducting a convergence study using graphics processing unit (GPU)-accelerated lattice simulations and show that numerical errors accumulated with time are significant in low-resolution scenarios, or in scenarios where the run-time causes the resolution to drop below the relevant scales in the problem. Our study determines that the dominant, growing high frequency peak of the gravitational wave signals in the fiducial "hill-top model" in [arXiv:1607.01314] is a numerical artifact. This new finding prompts the need for a more careful analysis of the numerical validity of other similar results related to gravitational waves from oscillon dynamics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗
DOE OSTI2016

The Influence of PV Module Materials and Design on Solder Joint Thermal Fatigue Durability

Finite element model (FEM) simulations have been performed to elucidate the effect of flat plate photovoltaic (PV) module materials and design on PbSn eutectic solder joint thermal fatigue durability. The statistical method of Latin Hypercube sampling was employed to investigate the sensitivity of simulated damage to each input variable. Variables of laminate material properties and their thicknesses were investigated. Using analysis of variance, we determined that the rate of solder fatigue was most sensitive to solder layer thickness, with copper ribbon and silicon thickness being the next two most sensitive variables. By simulating both accelerated thermal cycles (ATCs) and PV cell temperature histories through two characteristic days of service, we determined that the acceleration factor between the ATC and outdoor service was independent of the variables sampled in this study. This result implies that an ATC test will represent a similar time of outdoor exposure for a wide range of module designs. This is an encouraging result for the standard ATC that must be universally applied across all modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗