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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Search for Microscopic Black Hole Signatures at the Large Hadron Collider

A search for microscopic black hole production and decay in proton-proton collisions at a center-of-mass energy of 7 TeV has been conducted using Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider. A total integrated luminosity of 35 pb -1 data sample, taken by CMS Collaboration in year 2010, has been analyzed. A novel background estimation for multi-jet events beyond TeV scale has been developed. A good agreement with standard model backgrounds, dominated by multi-jet production, is observed for various final-state multiplicities. Using semi-classical approximation, upper limits on minimum black hole mass at 95% confidence level are set in the range of 3.5 - 4.5 TeV for values of the Planck scale up to 3 TeV. Model-independent limits are provided to further constrain microscopic black hole models with additional regions of parameter space, as well as new physics models with multiple energetic final states. These are the first limits on microscopic black hole production at a particle accelerator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Mathematical Modelling of Gas–Liquid, Two-Phase Flows in a Ladle Shroud

Differential and macroscopic models of argon–steel flows in ladle shroud have been developed. In this, argon–steel, two-phase flow phenomena have been formulated via a transient, three dimensional, turbulent flow model, based on the volume of fluid (VOF) calculation procedure. While realizable k–ε turbulence model has been applied to map turbulence, commercial, CFD software ANSYS-Fluent™ (Version 18), has been applied to carry out numerical calculations. Predictions from the model have been directly assessed against experimental measurements across the range of shroud dimensions and volumetric flow rates typically practiced in the industry. It is demonstrated that the two-phase turbulent flow model captures the general features of gas–liquid flows in ladle shroud providing estimates of free jet length and threshold gas flow rates (required to halt air ingression) which are in agreement with corresponding experimental measurements. In the absence of differential solutions, a macroscopic model has been worked out through dimensional analysis embodying multiple non-linear regression. It is shown that dimensionless free jet length in bloom and slab casting shrouds can be estimated reasonably accurately from the following correlation (in SI unit), viz., ((L{sub jet})/(D{sub CN}))=2.8×10{sup −2}(((Q{sub G})/(Q{sub L}))){sup 1.14}(((gD{sub CN}{sup 5})/(Q{sub L}{sup 2}))){sup 0.8}(((σD{sub CN}{sup 3})/(ρ{sub L}Q{sub L}{sup 2}))){sup −0.9}(((D{sub sh})/(D{sub CN}))){sup 2.0}(((ρ{sub G})/(ρ{sub L}))){sup −0.30}in which, L{sub jet} is the free liquid jet length (m), Q{sub G} is the gas flow rate (m{sup 3}/s), Q{sub L} is the liquid flow rate (m{sup 3}/s), D{sub sh} is shroud diameter (m), D{sub CN} is the collector nozzle diameter (m), σ is the interfacial tension (N/m), and ρ{sub G} as well as ρ{sub L} are respectively density of gas and liquid (kg/m{sup 3}). It is demonstrated that the proposed correlation is consistent with the laws of physical modeling and leads to estimates that are in good agreement with predictions from the differential models, for both air-water as well as argon–steel systems. Numerical simulations as well as macroscopic modeling have indicated that thermo-physical properties of the gas–liquid system are important and exert some influences on the gas–liquid, two-phase, flow in ladle shrouds, albeit not to a large extent. Despite dissimilar thermo-physical properties, full scale water modeling appears to be sufficiently predictive and provides reasonable macroscopic descriptions of the two-phase flow phenomena in industrial ladle shroud systems.

42 ENGINEERING↗
DOE OSTI2012

Concept to diagnose mix with imaging x-ray Thomson scattering

Turbulent mixing of two fluid species is a ubiquitous problem, prevalent in systems such as inertial confinement fusion (ICF) capsule implosions, supernova remnants, and other astrophysical systems. In complex, high Reynolds number compressible high energy density (HED) flows such as these, hydrodynamic instabilities initiate the turbulent mixing process, which can then feedback and alter the mean hydrodynamic motion through nonlinear processes. In order to predict how these systems evolve under turbulent conditions, models are used. However, these models require detailed quantitative data to validate and constrain their detailed physics models as well as improve them. Providing this much needed data is currently at the forefront of HED research but is proving elusive due to a lack of available diagnostics capable of directly measuring detailed flow variables. Thomson scattering is a promising technique in this regard as it provides fundamental conditions of the flow ({rho}, T, Zbar) due to its direct interaction with the small scales of the fluid or plasma and was recently considered as a possible mix diagnostic. With the development of imaging x-ray Thomson scattering (IXRTS) obtaining spatial profiles of these variables is within reach. We propose a novel use of the IXRTS technique that will provide more detailed quantitative data required for model validation in mix experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1991

Comprehensive model for the nucleus of Periodic Comet Tempel 2 and its activity

A comprehensive synergistic physical model for the nucleus of Periodic Comet Tempel 2 was developed on the basis of observations carried out in 1988. The model includes the best possible estimates of the comet's bulk properties (including the dimensions and the approximate shape), information on its state of rotation, and the characterization of its activity. The model is shown to be consistent with all lines of evidence that are currently available, including relevant information from earlier apparitions.

Sekanina, Zdenek↗
NASA NTRS2011

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to-7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Toure, Ally M.↗
DOE OSTI2025

High Fidelity Digital Twins for BWRX-300 Critical Systems

This project developed and demonstrated digital-twin technology for advanced nuclear systems under the ARPA-E GEMINA program, with a focus on the BWRX-300 small modular reactor. The primary objective was to reduce operation and maintenance (O&M) costs through predictive health monitoring and model-based fault detection. The project combined MIT’s high-fidelity physics models and machine-learning and digital-twin algorithms, and GE Hitachi’s digital-twin algorithms and operational expertise to build physics-based digital twins capable of real-time system monitoring and maintenance scheduling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

MELCOR Modeling of Non-LWR Systems Draft Report for the Nuclear Regulatory Commission

This report provides an overview of technical issues and design features relevant to advanced reactors and reviews MELCOR's current readiness for modeling accidents in such reactor types. This report describes advanced reactor physics models currently available or under development, and gauges the level of effort required to develop new models and capabilities applicable to assessing advanced reactor safety issues. Finally, this report reviews the available database that can be used in verification and validation of new models. Four general advanced reactor types are considered in this report: 1) High Temperature Gas-Cooled Reactor (HTGR); 2) Sodium Fast Reactor (SFR); 3) Molten Salt Reactor (MSR); 4) Fluoride Salt-Cooled High Temperature Reactor (FHR)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Development of Critical Experiments to Provide Validation Data for Multiphysics Coupling Methods (Final Report)

Computational simulation has become one of the most important tools in the modern toolbox for nuclear reactor design. Reactor simulations, like the reactors themselves, involve multiple physical processes including neutronics, fluid flow and heat transfer (thermal-hydraulics), thermo-mechanics, fuel behavior, chemistry, and balance of plant. Each process can affect the behavior of the others, and advanced computational tools have been developed that couple separate physics models together to simulate the feedback effects. The objective of this work is to generate experimental data to support the validation of multi-physics reactor codes. It specifically targets the SHARP toolset from the NEAMS Reactor Product Line, but it is intended to be useful in any set of coded that couples thermal-hydraulics with neutronics simulations. The project takes advantage of the inherent flexibility of the RPI Reactor Critical Facility (RCF), a low power open-pool research reactor. The standard configuration of the RCF is used to explore the effects of moderator temperature on neutronic behavior and reactivity. Because the low power of the reactor generates no appreciable temperature change in the moderator, the moderator temperature is artificially changed with two 18 kW electric immersion heaters. The temperature feedback effects can be observed by recording the change in the reactor power as the reactor goes from a supercritical state, to critical, to a subcritical state. To expand the temperature range and temperature rate of change that can be investigated, an experimental apparatus consisting of a heated water loop was designed and installed in the RCF. In this setup, water from a heated reservoir tank is pumped through a pipe placed through the center of the reactor core. The water in the reservoir can be heated to around 70 °C and pumped through the system. The pump has a variable flow rate and electronic control, so a number of scenarios can be developed. A slug of water can be pushed through the system to generate a reasonably fast change in reactivity. The pump speed can be increased or decreased over the course of a measurement, or even oscillated at different periods, to observe the response of the reactor. To complement the experimental data generated by the RCF, a set of computational models have been developed. They are intended to illustrate the usefulness of the experimental data generated in validating coupled multi-physics simulations. A model previously constructed in MCNP has been used in conjunction with safety analysis at the RCF for many years and was modified to support the design of the heated water loop experiment. This model was translated into Serpent 2, a similar Monte Carlo code, which provided the added advantage of being able to produce collapsed cross-section data files for use in the deterministic neutron transport codes that are part of the SHARP toolset. A complete reconstruction of the RCF model, both with and without the heated fluid loop experiment, were necessary for use with the SHARP codes, PROTEUS (neutronics) and Nek5000 (thermal hydraulics), as these require an unstructured mesh format to operate. The computational requirements for fully coupled three dimensional simulations are significant, and challenges to full implementation and comparison with RCF experiments remain. A wide variety of experiments have been conducted for eventual benchmarking and validation of multi-physics reactor codes. A complete database of detailed time- and temperature-dependent measurements have been generated and stored. Several examples of the experimental results are presented here, with refinement and analysis of experiments ongoing. While these experiments still only address a finite range of operating conditions, in particular being limited to water in the sub-boiling regime, they represent a unique (to date) look at the feedback between temperature and neutronic behavior suited for validation of the coupling routines in multi-physics codes. It is hoped that these will be the foundation of future benchmarks that will provide confidence in the tools that will be used to develop the next generation of nuclear reactor systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Gibbs Measures Over Locally Tree-Like Graphs and Percolative Entropy Over Infinite Regular Trees

Consider a statistical physical model on the d-regular infinite tree T{sub d} described by a set of interactions Φ. Let {G_n} be a sequence of finite graphs with vertex sets V{sub n} that locally converge to T{sub d}. From Φ one can construct a sequence of corresponding models on the graphs G{sub n}. Let {μ_n} be the resulting Gibbs measures. Here we assume that {μ_n} converges to some limiting Gibbs measure μ on T{sub d} in the local weak{sup ∗} sense, and study the consequences of this convergence for the specific entropies |V{sub n}|{sup −1}H(μ{sub n}). We show that the limit supremum of |V{sub n}|{sup −1}H(μ{sub n}) is bounded above by the percolative entropyH{sub perc}(μ), a function of μ itself, and that |V{sub n}|{sup −1}H(μ{sub n}) actually converges to H{sub perc}(μ) in case Φ exhibits strong spatial mixing on T{sub d}. When it is known to exist, the limit of |V{sub n}|{sup −1}H(μ{sub n}) is most commonly shown to be given by the Bethe ansatz. Percolative entropy gives a different formula, and we do not know how to connect it to the Bethe ansatz directly. We discuss a few examples of well-known models for which the latter result holds in the high temperature regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

On the dynamics and kinematics of two-fluid-phase flow in porous media

A model formulated in terms of both conservation and kinematic equations for phases and interfaces in two-fluid-phase flow in a porous medium system is summarized. Macroscale kinematic equations are derived as extensions of averaging theorems and do not rely on conservation principles. Models based on both conservation and kinematic equations can describe multiphase flow with varying fidelity.When only phase-based equations are considered, a model similar in form to the traditional model for two-fluid-phase flow results. When interface conservation and kinematic equations are also included, a novel formulation results that naturally includes evolution equations that express dynamic changes in fluid saturations, pressures, the capillary pressure, and the fluid-fluid interfacial area density in a two-fluid-system. This dynamic equation set is unique to this work, and the importance of the modeled physics is shown through both microfluidic experiments and high-resolution lattice Boltzmann simulations. The validation work shows that the relaxation of interface distribution and shape toward an equilibrium state is a slow process relativeto the time scale typically allowed for a system to approach an apparent equilibrium state based upon observations of fluid saturations and external pressure measurements. Consequently, most pressure-saturation data intended to denote an equilibrium state are likely a sampling from a dynamic system under-going changes of interfacial curvatures that are not typically monitored. The results confirm the importance of kinematic analysis in combination with conservation equations for faithful modeling of system physics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enabling Strong Neutrino Self-Interaction with an Unparticle Mediator

Recent explorations of the cosmic microwave background and the large-scale structure of the universe have indicated a preference for sizable neutrino self-interactions, much stronger than what the standard model offers. When interpreted in the context of simple particle-physics models with a light, neutrinophilic scalar mediator, some of the hints are already in tension with the combination of terrestrial, astrophysical, and cosmological constraints. We take a novel approach by considering neutrino self-interactions through a mediator with a smooth, continuous spectral density function. We consider Georgi’s unparticle with a mass gap as a concrete example and point out two useful effects for mitigating two leading constraints. (i) The Unparticle is “broadband’—it occupies a wide range of masses which allows it to pass the early universe constraint on effective number of extra neutrinos ( Δ N eff ) even if the mass gap lies below the MeV scale. (ii) Scattering involving unparticles is less resonant, which lifts the constraint set by IceCube based on a recent measurement of ultra-high-energy cosmogenic neutrinos. Our analysis shows that an unparticle mediator can open up ample parameter space for strong neutrino self-interactions of interest to cosmology and serves a well-motivated target for upcoming experiments. Published by the American Physical Society 2025

Foroughi-Abari, Saeid (ORCID:0000000294061896)↗
NASA NTRS2011

A New Aerodynamic Data Dispersion Method for Launch Vehicle Design

A novel method for implementing aerodynamic data dispersion analysis is herein introduced. A general mathematical approach combined with physical modeling tailored to the aerodynamic quantity of interest enables the generation of more realistically relevant dispersed data and, in turn, more reasonable flight simulation results. The method simultaneously allows for the aerodynamic quantities and their derivatives to be dispersed given a set of non-arbitrary constraints, which stresses the controls model in more ways than with the traditional bias up or down of the nominal data within the uncertainty bounds. The adoption and implementation of this new method within the NASA Ares I Crew Launch Vehicle Project has resulted in significant increases in predicted roll control authority, and lowered the induced risks for flight test operations. One direct impact on launch vehicles is a reduced size for auxiliary control systems, and the possibility of an increased payload. This technique has the potential of being applied to problems in multiple areas where nominal data together with uncertainties are used to produce simulations using Monte Carlo type random sampling methods. It is recommended that a tailored physics-based dispersion model be delivered with any aerodynamic product that includes nominal data and uncertainties, in order to make flight simulations more realistic and allow for leaner spacecraft designs.

Pinier, Jeremy T.↗
DOE OSTI2011

Model of the self-Q-switching instability of passively phased fiber laser arrays

A simple physical model is presented to explain observed self-pulsations in passively phased rare earth-doped fiber laser arrays. Their essential features are the feedback level s sensitivity to small perturbations in the phases of array fields, hence altering the cavity s Q-value, and the nonlinear changes in the refractive index of the amplifier gain media. The model s qualitative prediction for an array having at least two elements that is operated at sufficiently high power levels, of the growth of an initial disturbance, is confirmed by a linearized stability analysis of the field and medium equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

Internet Based Simulations of Debris Dispersion of Shuttle Launch

The debris dispersion model (which dispersion model?) is so heterogeneous and interrelated with various factors, 3D graphics combined with physical models are useful in understanding the complexity of launch and range operations. Modeling and simulation in this area mainly focuses on orbital dynamics and range safety concepts, including destruct limits, telemetry and tracking, and population risk. Particle explosion modeling is the process of simulating an explosion by breaking the rocket into many pieces. The particles are scattered throughout their motion using the laws of physics eventually coming to rest. The size of the foot print explains the type of explosion and distribution of the particles. The shuttle launch and range operations in this paper are discussed based on the operations of the Kennedy Space Center, Florida, USA. Java 3D graphics provides geometric and visual content with suitable modeling behaviors of Shuttle launches.

Bardina, Jorge↗
DOE OSTI2025

Benchmarking Monte Carlo codes for the modelling of low-energy neutron production target reactions

The increasing adoption of accelerator-based neutron sources (ABNS) for applications including neutron capture therapy (NCT) research has highlighted the need for accurate simulation tools. Precise modelling of the neutron production target is crucial to ensure that simulated predictions of neutron beam characteristics used for subsequent beam shaping assembly design are reliable. This work presents a comprehensive benchmarking of four widely-used Monte Carlo codes - Geant4, PHITS, FLUKA (CERN), and MCNP - for modelling low-energy neutron production target reactions. Using their recommended physics models and cross-section libraries, we evaluate each code’s performance in simulating four beam-target reactions: 7 Li(p,n) 7 Be, 9 Be(p,n) 9 B, 9 Be(d,n) 10 B, and C(d,n)N. Predictions of neutron yield, angular distributions, and energy spectra are compared against available thick target experimental data. Results show varying levels of agreement between the codes depending on the reaction type, energy range, and beam characteristics. Geant4, MCNP and PHITS are the overall best performing codes for the simulation of total neutron yield and yield in the forward direction across most reactions. Across energies where experimental benchmarks exist, inter-code discrepancies in total and forward-directed yield are typically 10 to 30%, with larger deviations at near-threshold incident ion energies. PHITS provides the best overall reproduction of experimental spectra, particularly for the 9 Be(p,n) 9 B reaction. Additionally, PHITS demonstrates superior computational performance for most reactions. These findings provide valuable guidance for ABNS design, highlighting the strengths and limitations of each code for the simulation of low-energy neutron production reactions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production

Machine learning is frequently applied in the wind energy industry to build statistical models of wind farm power production using atmospheric data as input. In the field of wind power forecasting, in particular, there has been substantial research into finding the best-performing learning algorithms that improve model predictions. Overlooked in the literature, however, is the influence of atmospheric turbulence and stability measurements in improving model predictions. It has been well-established through observations and physical models that these effects can have considerable influence on wind farm power production; yet consideration of these effects in statistical models is almost entirely absent from the literature. In this work, we examine the impact of atmospheric turbulence and stability inputs on statistical model predictions of wind farm power output. Hourly observations from a wind farm in the Pacific Northwest United States located in very complex terrain are used. Five common learning algorithms and nine atmospheric variables are considered, five of which represent some measure of turbulence or stability. Here, we find a considerable improvement in hourly power predictions when some measure of turbulence or stability is included in the model. In particular, turbulent kinetic energy was found to be the most important variable apart from wind speed and more important than wind direction, pressure, and temperature. By contrast, the choice of learning algorithm is shown to be relatively less important in improving predictions. Based on this work, we recommend that turbulence and stability variables become standard inputs into statistical models of wind farm power production.

17 WIND ENERGY↗