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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understanding the fusion yield dependencies in OMEGA DT-layered implosion experiments using a physics-based statistical mapping model

Improving the performance of inertial confinement fusion implosions requires physics models that can accurately predict the response to changes in the experimental inputs. Good predictive capability has been demonstrated for the fusion yield using a statistical mapping of simulated outcomes to experimental data [Gopalaswamy et al., Nature 565(771), 581–586 (2019)]. In this paper, a physics-based statistical mapping approach is used to extract and quantify all the major sources of degradation of fusion yield for direct-drive implosions on the OMEGA laser. Here, the yield is found to be dependent on the age of the deuterium tritium fill, the ℓ = 1 asymmetry in the implosion core, the laser beam-to-target size ratio, and parameters related to the hydrodynamic stability. A controlled set of experiments were carried out where only the target fill age was varied while keeping all other parameters constant. The measurements were found to be in excellent agreement with the fill age dependency inferred using the mapping model. In addition, a new implosion design was created, guided by the statistical mapping model by optimizing the trade-offs between increased laser energy coupling at larger target size and the degradations caused by the laser beam-to-target size ratio and hydrodynamic instabilities. When experimentally performed, an increased fusion yield was demonstrated in targets with larger diameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Physics-constrained deep learning of nonlinear normal modes of spatiotemporal fluid flow dynamics

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.

Mechanics↗
DOE OSTI2023

Flexible, integrated modeling of tokamak stability, transport, equilibrium, and pedestal physics

The STEP (Stability, Transport, Equilibrium, and Pedestal) integrated-modeling tool has been developed in OMFIT to predict stable, tokamak equilibria self-consistently with core-transport and pedestal calculations. STEP couples theory-based codes to integrate a variety of physics, including magnetohydrodynamic stability, transport, equilibrium, pedestal formation, and current-drive, heating, and fueling. The input/output of each code is interfaced with a centralized ITER-Integrated Modelling & Analysis Suite data structure, allowing codes to be run in any order and enabling open-loop, feedback, and optimization workflows. This paradigm simplifies the integration of new codes, making STEP highly extensible. STEP has been verified against a published benchmark of six different integrated models. Core-pedestal calculations with STEP have been successfully validated against individual DIII-D H-mode discharges and across more than 500 discharges of the H98,y2 database, with a mean error in confinement time from experiment less than 19%. STEP has also reproduced results in less conventional DIII-D scenarios, including negative-central-shear and negative-triangularity plasmas. Predictive STEP modeling has been used to assess performance in several tokamak reactors. Simulations of a high-field, large-aspect-ratio reactor show significantly lower fusion power than predicted by a zero-dimensional study, demonstrating the limitations of scaling-law extrapolations. STEP predictions have found promising scenarios for an EXhaust and Confinement Integration Tokamak Experiment, including a high-pressure, 80%-bootstrap-fraction plasma. ITER modeling with STEP has shown that pellet fueling enhances fusion gain in both the baseline and advanced-inductive scenarios. Finally, STEP predictions for the SPARC baseline scenario are in good agreement with published results from the physics basis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The physics of gain relevant to inertial fusion energy target designs

In inertial confinement fusion, pellets of deuterium tritium fuel are compressed and heated to the conditions where they undergo fusion and release energy. The target gain (ratio of energy released from the fusion reactions to the energy in the drive source) is a key parameter in determining the power flow and economics of an inertial fusion energy (IFE) power plant. In this study, the physics of gain is explored for laser-direct-drive targets with driver energy at the megajoule scale. This analysis is performed with the assumption of next-generation laser technologies that are expected to increase convergent drive pressures to over 200 Mbar. This is possible with the addition of bandwidth to the laser spectrum and by employing focal-spot zooming. Simple physics arguments are used to derive scaling laws that describe target gain as a function of laser energy, adiabat, ablation pressure, and implosion velocity. Scaling laws are found for the unablated mass, ablation pressure, areal density, implosion velocity, and in-flight aspect ratio. Furthermore, those scaling laws are then used to explore the design space for IFE targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Physically realistic, parametric model for excitonic critical point parabolic band oscillators

Critical point parabolic band (CPPB) oscillators are often useful to model the optical response of semiconductor materials, such as hybrid organic–inorganic lead halide-based perovskites, to incident photons in the form of the complex dielectric function (ε=ε1+iε2) spectra. Some models of ε using CPPB oscillators are not guaranteed Kramers–Kronig (KK) consistent (and therefore not physically realistic), may have excess or arbitrary parameter values, or may require prohibitively long computational time when used to fit ellipsometric spectra. For excitonic CPPBs, commonly used to describe the optical response of hybrid organic–inorganic lead halide-based perovskite materials, a physically realistic, parametric model of ε is developed from the KK relationship between ε1 and ε2 for a number of CPPB oscillators with an Urbach tail below the lowest direct transition. This parametric model is shown to produce the same line shape reported from previous works accurately and more quickly than other available KK-consistent CPPB models.

Physics↗
DOE OSTI2024

Grad–Shafranov equilibria via data-free physics informed neural networks

A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. In this paper, we explore a method for solving the Grad–Shafranov equation by using physics-informed neural networks (PINNs). For PINNs, we optimize neural networks by directly minimizing the residual of the partial differential equation as a loss function. We show that PINNs can accurately and effectively solve the Grad–Shafranov equation with several different boundary conditions, making it more flexible than traditional solvers. This method is flexible as it does not require any mesh and basis choice, thereby streamlining the computational process. We also explore the parameter space by varying the size of the model, the learning rate, and boundary conditions to map various tradeoffs such as between reconstruction error and computational speed. Additionally, we introduce a parameterized PINN framework, expanding the input space to include variables such as pressure, aspect ratio, elongation, and triangularity in order to handle a broader range of plasma scenarios within a single network. Parameterized PINNs could be used in future work to solve inverse problems such as shape optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A physics-constrained deep learning treatment of runaway electron dynamics

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker–Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained, the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Abraham Szöke [Abraham Szoke - Physics Today Obituary]

Abraham Szöke, who did groundbreaking work in quantum optics, laser fusion, high-energy-density (HED) physics, and x-ray crystallography, died from complications of lymphoma on 26 January 2017. Born in Budapest, Hungary, on 1 December 1929, Szöke was an excellent student in mathematics and science who placed near the top of the famous Eötvös mathematics competition. He survived the Holocaust, and in 1949 he escaped from Communist Hungary and emigrated to Israel. He earned his doctorate in physics, with Saul Meiboom and William Low as his advisers, in 1962 from the Hebrew University of Jerusalem. As part of his thesis, he made the first measurement demonstrating the effect of radiation damping on nuclear magnetic resonance and its relation to transient maser action; that effect had been predicted a few years earlier by Nicolaas Bloembergen and Robert Pound.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Experimental modelling of the physical process of laser tattoo removal

Two model objects are proposed to study the physical mechanism of tattoo removal using nano-, pico-, and femtosecond laser pulses. Experimental results on laser decolourisation of cotton fabric coloured with black tattoo ink and a pig skin sample with tattoo ink deposited on its rear side, in dependence of the duration, energy, and number of laser pulses and the laser spot size, are presented and discussed. It is established that decolourisation of cotton fabric and pig skin without their destruction occurs under irradiation with ultrashort laser pulses and is absent in the case of nanosecond pulses. An analysis of the results obtained has made it possible to draw a number of conclusions, useful for gaining a better insight into the physical mechanism of laser tattoo removal. (paper)

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

Here, this work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

Machine learning↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2016

LHC Forward Physics

The goal of this report is to give a comprehensive overview of the rich field of forward physics, with a special attention to the topics that can be studied at the LHC. The report starts presenting a selection of the Monte Carlo simulation tools currently available, chapter 2, then enters the rich phenomenology of QCD at low, chapter 3, and high, chapter 4, momentum transfer, while the unique scattering conditions of central exclusive production are analyzed in chapter 5. The last two experimental topics, Cosmic Ray and Heavy Ion physics are presented in the chapter 6 and 7 respectively. Chapter 8 is dedicated to the BFKL dynamics, multiparton interactions, and saturation. The report ends with an overview of the forward detectors at LHC. Each chapter is correlated with a comprehensive bibliography, attempting to provide to the interested reader with a wide opportunity for further studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Photoassisted physical vapor epitaxial growth of semiconductors: a review of light-induced modifications to growth processes

Herein, we review the remarkable range of modifications to materials properties associated with photoexcitation of the growth surface during physical vapor epitaxy of semiconductors. We concentrate on mechanisms producing measureable, utilizable changes in crystalline perfection, phase, composition, doping, and defect distribution. We outline the relevant physics of different mechanisms, concentrating on those yielding effects orthogonal to the primary growth variables of temperature and atomic or molecular fluxes and document the phenomenological effects reported. Based on experimental observations from a range of semiconductor systems and growth conditions, the primary effects include enhanced anion desorption, molecular dissociation, increased doping efficiency, modification to defect populations and improvements to the crystalline quality of epilayers grown at low temperatures. Future research directions and technological applications are also discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Dreaming awake: disentangling the underlying physics in case of a SUSY-like discovery at the LHC

The purpose of this review is to investigate what kind of physics can be extracted at the Large Hadron Collider (LHC), assuming a discovery is made in events with missing transverse momentum, as generically expected in supersymmetry (SUSY) with R-parity conservation. To set the scene, we first discuss the collider phenomenology of the six possible electroweakino benchmark scenarios, as they provide valuable insight into what one might be facing at the LHC. We review the existing methods for mass reconstruction from measured kinematic endpoints in the distributions of suitable variables, e.g. the invariant masses of various sets of visible decay products, as well as the M T2 and the M 2 types of variables. We propose to extend the application of these methods to the various topologies of fully hadronic final states, possibly with hadronically reconstructed massive bosons (W, Z or h). We test the idea with a simplified simulation of events in the main electroweakino benchmark scenarios. We find that the fully hadronic events allow the complete determination of the relevant mass spectrum. For comparison, we also review the potential of the standard kinematic endpoint methods for final states involving leptons from the decays of (on-shell or off-shell) sleptons. We find that with 300 fb -1 , the statistics for the leptonic events is very marginal and they look less promising than the fully hadronic channels. This corresponds to a complete reversal of the usual paradigm, where leptonic events comprised the gold-plated SUSY channels. Finally, we put together all available information and summarize what level of understanding of the underlying physics can be achieved. We show that, as a by-product of the mass reconstruction, it is also possible to determine the production cross sections and decay branching ratios, which in turn enable us to pinpoint the underlying model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Optimization and supervised machine learning methods for fitting numerical physics models without derivatives

Here, we address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations on the number of concurrent model evaluations that can be performed. As a case study, we consider the Fayans energy density functional model, which has characteristics similar to many model fitting and calibration problems in nuclear physics. We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Operational space and performance limiting events in the first physics campaign of MAST-U

Abstract The MAST-U fusion plasma research device, an upgrade to the Mega Amp Spherical Tokamak, has recently completed its first campaign of physics operation. MAST-U operated with Ohmic, or one or two neutral beams for heating, at 400–800 kA plasma current, in conventional or ‘SuperX’ divertor configurations. Equilibrium reconstructions provide key plasma physics parameters vs. time for each discharge, and diagrams are produced which show where the prevalence of operation occurs as well as the limits in various operational spaces. When compared to stability limits, the operation of MAST-U so far has generally stayed out of the low q , low density instability region, and below the high density Greenwald limit, high beta global stability limit, and high elongation vertical stability limit. MAST-U still has the potential to reach higher elongation, which could benefit the plasma performance. Despite the majority of operations happening below established stability limits, disruptions do occur in the flat-top phase of MAST-U plasmas. The reasons for these disruptions are highlighted, and possible strategies to avoid them and to extend the operational space of MAST-U in future campaigns are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Assessing the numerical stability of physics models to equilibrium variation through database comparisons on DIII-D

High fidelity kinetic equilibria are crucial for tokamak modeling and analysis. Manual workflows for constructing kinetic equilibria are time consuming and subject to user error, motivating development of automated equilibrium reconstruction tools to provide accurate and consistent reconstructions for downstream physics analysis. These automated tools also provide access to kinetic equilibria at large database scales, which enables the quantification of general uncertainties arising from equilibrium reconstruction techniques. In this paper, we compare a large database of DIII-D kinetic equilibria generated manually by physics experts to equilibria from automated kinetic reconstruction tools, assessing the impact of reconstruction method on equilibrium parameters and resulting magnetohydrodynamic stability calculations. We find agreement among scalar parameters, whereas profile quantities, such as the bootstrap current, show larger disagreements. We analyze ideal kink and classical tearing stability with DCON and STRIDE respectively, finding that the kink stability calculation is generally more robust than the tearing index Δ' calculation. We find that in 90% of cases, both kink stability classifications are unchanged between the manual expert and automated kinetic equilibria.

CAKE↗
DOE OSTI2018

Physics with Reactor Neutrinos

Neutrinos produced by nuclear reactors have played a major role in advancing our knowledge of the properties of neutrinos. The first direct detection of the neutrino, confirming its existence, was performed using reactor neutrinos. More recent experiments utilizing reactor neutrinos have also found clear evidence for neutrino oscillation, providing unique input for the determination of neutrino mass and mixing. Ongoing and future reactor neutrino experiments will explore other important issues, including the neutrino mass hierarchy and the search for sterile neutrinos and other new physics beyond the standard model. In this paper, we review the recent progress in physics using reactor neutrinos and the opportunities they offer for future discoveries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗