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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 OSTI2014

Noble gas encapsulation into carbon nanotubes: Predictions from analytical model and DFT studies

The energetics for the interaction of the noble gas atoms with the carbon nanotubes (CNTs) are investigated using an analytical model and density functional theory calculations. Encapsulation of the noble gas atoms, He, Ne, Ar, Kr, and Xe into CNTs of various chiralities is studied in detail using an analytical model, developed earlier by Hill and co-workers. The constrained motion of the noble gas atoms along the axes of the CNTs as well as the off-axis motion are discussed. Analyses of the forces, interaction energies, acceptance and suction energies for the encapsulation enable us to predict the optimal CNTs that can encapsulate each of the noble gas atoms. We find that CNTs of radii 2.98 − 4.20 Å (chiral indices, (5,4), (6,4), (9,1), (6,6), and (9,3)) can efficiently encapsulate the He, Ne, Ar, Kr, and Xe atoms, respectively. Endohedral adsorption of all the noble gas atoms is preferred over exohedral adsorption on various CNTs. The results obtained using the analytical model are subsequently compared with the calculations performed with the dispersion-including density functional theory at the M06 − 2X level using a triple-zeta basis set and good qualitative agreement is found. The analytical model is however found to be computationally cheap as the equations can be numerically programmed and the results obtained in comparatively very less time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

CMB weak-lensing beyond the Born approximation: a numerical approach

We perform a complete study of the gravitational lensing effect beyond the Born approximation on the Cosmic Microwave Background (CMB) anisotropies using a multiple-lens raytracing technique through cosmological N-body simulations of the DEMNUni suite. The impact of second-order effects accounting for the non-linear evolution of large-scale structures is evaluated propagating for the first time the full CMB lensing jacobian together with the light rays trajectories. We carefully investigate the robustness of our approach against several numerical effects in the raytracing procedure and in the N-body simulation itself, and find no evidence of large contaminations. We discuss the impact of beyond-Born corrections on lensed CMB observables, and compare our results with recent analytical predictions that appeared in the literature, finding a good agreement, and extend these results to smaller angular scales. We measure the gravitationally-induced CMB polarization rotation that appears in the geodesic equation at second order, and compare this result with the latest analytical predictions. We then present the detection prospect of beyond-Born effects with the future CMB-S4 experiment. We show that corrections to the temperature power spectrum can be measured only if a good control of the extragalactic foregrounds is achieved. Conversely, the beyond-Born corrections on E and B-modes power spectra will be much more difficult to detect.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗
DOE OSTI2015

FAST MAGNETIC FIELD AMPLIFICATION IN THE EARLY UNIVERSE: GROWTH OF COLLISIONLESS PLASMA INSTABILITIES IN TURBULENT MEDIA

In this work we report on a numerical study of the cosmic magnetic field amplification due to collisionless plasma instabilities. The collisionless magnetohydrodynamic equations derived account for the pressure anisotropy that leads, in specific conditions, to the firehose and mirror instabilities. We study the time evolution of seed fields in turbulence under the influence of such instabilities. An approximate analytical time evolution of the magnetic field is provided. The numerical simulations and the analytical predictions are compared. We found that (i) amplification of the magnetic field was efficient in firehose-unstable turbulent regimes, but not in the mirror-unstable models; (ii) the growth rate of the magnetic energy density is much faster than the turbulent dynamo; and (iii) the efficient amplification occurs at small scales. The analytical prediction for the correlation between the growth timescales and pressure anisotropy is confirmed by the numerical simulations. These results reinforce the idea that pressure anisotropies—driven naturally in a turbulent collisionless medium, e.g., the intergalactic medium, could efficiently amplify the magnetic field in the early universe (post-recombination era), previous to the collapse of the first large-scale gravitational structures. This mechanism, though fast for the small-scale fields (∼kpc scales), is unable to provide relatively strong magnetic fields at large scales. Other mechanisms that were not accounted for here (e.g., collisional turbulence once instabilities are quenched, velocity shear, or gravitationally induced inflows of gas into galaxies and clusters) could operate afterward to build up large-scale coherent field structures in the long time evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Verification of particle simulation of radio frequency waves in fusion plasmas

Radio frequency (RF) waves can offer heating, current and flow drive, as well as instability control for steady state operations of fusion experiments. A particle simulation model has been designed in this work to provide a first-principles tool for studying the RF nonlinear interactions with plasmas. In this model, ions are considered as fully kinetic particles using the Vlasov equation and electrons are treated as guiding centers using the drift kinetic equation. This model has been implemented in a global gyrokinetic toroidal code using real electron-to-ion mass ratio. To confirm the model, linear simulations of ion plasma oscillation, ion Bernstein wave, and lower hybrid wave are carried out in cylindrical geometry and found to agree well with analytic predictions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Extragalactic photon-ALP conversion at CTA energies

Magnetic fields in extragalactic space between galaxy clusters may induce conversions between photons and axion-like particles (ALPs), thereby shielding the photons from absorption on the extragalactic background light. For TeV gamma rays, the oscillation length (l osc ) of the photon-ALP system becomes inevitably of the same order as the coherence length of the magnetic field l and the length over which the field changes significantly (transition length l t ) due to refraction on background photons. We derive exact statistical evolution equations for the mean and variance of the photon and ALP transfer functions in the non-adiabatic regime (l osc ~ l >> l t ). We also make analytical predictions for the transfer functions in the quasi-adiabatic regime (l osc < t ). Our results are important in light of the upcoming Cherenkov Telescope Array (CTA), and may also be applied to models with non-zero ALP masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Pre-nebular Light Curves of SNe I

We compare analytic predictions of supernova light curves with recent high-quality data from SN2011fe (Ia), KSN2011b (Ia), and the Palomar Transient Factory and the La Silla-QUEST variability survey (LSQ) (Ia). Because of the steady, fast cadence of observations, KSN2011b provides unique new information on SNe Ia: the smoothness of the light curve, which is consistent with significant large-scale mixing during the explosion, possibly due to 3D effects (e.g., Rayleigh–Taylor instabilities), and provides support for a slowly varying leakage (mean opacity). For a more complex light curve (SN2008D, SN Ib), we separate the luminosity due to multiple causes and indicate the possibility of a radioactive plume. The early rise in luminosity is shown to be affected by the opacity (leakage rate) for thermal and non-thermal radiation. A general derivation of Arnett’s rule again shows that it depends upon all processes heating the plasma, not just radioactive ones, so that SNe Ia will differ from SNe Ibc if the latter have multiple heating processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Additive manufacturing of sandwich panels with continuous fiber reinforced high modulus composite facings

Abstract An improved approach consisting of a combination of fiber placement and fused filament fabrication is introduced for the additive manufacture (AM) of structural grade sandwich beams. Here, sandwich beams are additively manufactured using in‐situ deposition and consolidation of continuous fiber unidirectional facings made from a commingled yarn system of e‐glass fiber (~50% vol.) and amorphous PET, and a hexagonal honeycomb core structure made from PETG. Both facings and the sandwich core are manufactured on a single machine, in one sequence (skin‐core‐skin), employing the benefit of matrix compatibility to create autohesion at the interfaces. Flexural and transverse shear rigidity are determined experimentally and compared with analytical predictions and show correlation to within 3%. Flexural strength and core shear strength are also measured. Post‐mortem examinations show that core fracture and core facing debond were the dominant failure mode in flexure. Single cantilever beam tests were performed to evaluate core facing debond toughness. Subsequently, surface preheat using infrared heaters was utilized to increase autohesion between core and facing. The results show debond toughness was increased 4 times using infrared heating. This research effort presents a manufacturing approach that has the potential for the AM of stiff, well bonded, structural grade sandwich beams, in an integrated sequence, employing in‐situ consolidation to the facings, without the need for the use of intermediate adhesives for skin‐to‐core bonding. Highlights An improved additive manufacturing technique for making sandwich panels is developed. Sandwich panel facings have fiber volume fractions of approximately 50%. Surface preheat improves core‐to‐facing debond toughness by a factor of 4. Top and bottom facings are consolidated during manufacture leading to better properties. Experimental results are compared to analytical predictions and show good correlation.

17 WIND ENERGY↗
DOE OSTI2015

Modifying mixing and instability growth through the adjustment of initial conditions in a high-energy-density counter-propagating shear experiment on OMEGA

Counter-propagating shear experiments conducted at the OMEGA Laser Facility have been evaluating the effect of target initial conditions, specifically the characteristics of a tracer foil located at the shear boundary, on Kelvin-Helmholtz instability evolution and experiment transition toward nonlinearity and turbulence in the high-energy-density (HED) regime. Experiments are focused on both identifying and uncoupling the dependence of the model initial turbulent length scale in variable-density turbulence models of k-ϵ type on competing physical instability seed lengths as well as developing a path toward fully developed turbulent HED experiments. We present results from a series of experiments controllably and independently varying two initial types of scale lengths in the experiment: the thickness and surface roughness (surface perturbation scale spectrum) of a tracer layer at the shear interface. We show that decreasing the layer thickness and increasing the surface roughness both have the ability to increase the relative mixing in the system, and thus theoretically decrease the time required to begin transitioning to turbulence in the system. In addition, we also show that we can connect a change in observed mix width growth due to increased foil surface roughness to an analytically predicted change in model initial turbulent scale lengths.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

WHEN SHOCK WAVES COLLIDE

Supersonic outflows from objects as varied as stellar jets, massive stars, and novae often exhibit multiple shock waves that overlap one another. When the intersection angle between two shock waves exceeds a critical value, the system reconfigures its geometry to create a normal shock known as a Mach stem where the shocks meet. Mach stems are important for interpreting emission-line images of shocked gas because a normal shock produces higher postshock temperatures, and therefore a higher-excitation spectrum than does an oblique shock. In this paper, we summarize the results of a series of numerical simulations and laboratory experiments designed to quantify how Mach stems behave in supersonic plasmas that are the norm in astrophysical flows. The experiments test analytical predictions for critical angles where Mach stems should form, and quantify how Mach stems grow and decay as intersection angles between the incident shock and a surface change. While small Mach stems are destroyed by surface irregularities and subcritical angles, larger ones persist in these situations and can regrow if the intersection angle changes to become more favorable. The experimental and numerical results show that although Mach stems occur only over a limited range of intersection angles and size scales, within these ranges they are relatively robust, and hence are a viable explanation for variable bright knots observed in Hubble Space Telescope images at the intersections of some bow shocks in stellar jets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Occupancy data analytics and prediction: A case study

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Furthermore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2015

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

High Performance Data Analytics on a Commodity Private Cloud

Sandia is researching novel methods of analysis for large datasets. The term “Big Data” has been used frequently in recent years to describe a discipline of computer science that attempts to distil an increasing quantity of data into a result or meaningful subset that can be easily visualized and acted upon by human consumers. These data are frequently characterized by one or more of the following: high volume, velocity, variety, and veracity. Sandia scientists and researchers are no strangers to large datasets, which often typify the result of high-performance computing workloads. The challenge for Sandia data scientists, however, comes at the arrival of exascale computing, which has become mandated by executive order for national computing centers. Because of power and networking limitations, exascale computing precludes the movement of datasets at exascale from data production to data analysis. Such analysis must be done in-situ, to mitigate the time and power costs of network transport. Currently, many of Sandia’s data analytics environments exist as independent entities, often specialized for individual use cases, interconnected to other data analytics, high-performance computing, and storage systems. Sandia is procuring, installing, and researching future platforms for such data analytics. A memory footprint of 256 GB per node, fourteen data rate (FDR) InfiniBand networking, PCIe connected non-volatile memory (NVMe) data storage, and an object data store will characterize the coming institutional data analytics platforms. The combined systems will contain 300 compute nodes. A cloud-inspired software stack resulting from collaboration between the Sandia centers will allow the systems to support a flexible mix of user and system-facing workloads and use cases including code development and testing in R and Python, Apache Hadoop and Spark, distributed in-memory databases, graph analytics, emulytics, system log and predictive analytics, and data visualization for institutional and mission customers. These systems will arrive at the end of FY 2015 and production is planned by the spring of FY 2016.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DIGITAL APPLICATIONS USING REAL-TIME VEHICLE EXHAUST INFORMATION

Vehicle emission is a major source of air pollution that causes a significant number of deaths globally. It has a profound impact on energy and the environment as well. The existing vehicle emission monitoring system is unable to help mitigating the pollution properly and therefore, requires precise real-time pollution measurement. The purpose of this paper is to discuss novel applications using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. Today, it is possible to measure such emission due to the emergence of affordable digital technologies such as the Internet of Things (IoT), wireless connectivity, cloud platform, and artificial intelligence. This paper discusses how digital technologies can be used for real-time monitoring of NOx gas as a measure of vehicle emission and predictive analytics applications. A description of data collection and pre-processing methodologies, actual collected data, and an approach to identify patterns between inputs such as vehicle speed and altitude and output such as NOx emission are included. Applying a simple neural network has produced promising results and is a first step towards developing predictive applications.

Digital, Vehicle Exhaust, IoT, AI, 1.4.2, Predicti↗
DOE OSTI2021

Additively Manufactured Compliant Hybrid Gas Thrust Bearing for Supercritical Carbon Dioxide Turbomachinery: Experimental Evaluation and Fluid–Structure Model Predictions

This paper presents rotating test results and advances an analytical predictive fluid–structure model for a new type of gas-lubricated thrust bearing fabricated using direct metal laser melting. The bearing concept in this study is a compliant hybrid gas thrust bearing that uses external pressurization to increase load carrying capacity, where the testing campaign in this study was only focused on steady-state static performance. The need for the bearing concept comes from enabling highly efficient supercritical carbon dioxide (sCO2) turbomachinery by replacing oil-lubricated bearings with process gas lubrication. Leveraging the process gas of the turbomachine for bearing lubrication results in lowered bearing power loss, simplified mechanical design, and allows for novel oil-free hermetic drivetrains resulting in an efficient emission-free system. The new concept utilizes hydrostatic pressurization on individual tilting pads flexibly mounted with hermetic squeeze film dampers (HSFDs). This paper focuses on rotating tests of a 173 mm outer diameter gas thrust bearing in air up to 10 krpm and hydrostatic inlet pressures to 365 psi (2.52 MPa). The influence of thrust runner speed and bearing inlet pressure on force deflection characteristics and load carrying capability of the gas film were experimentally evaluated. This work also advances a predictive fluid–structure thrust bearing model using an isothermal ideal-gas-based compressible Reynolds flow equation directly coupled to a lumped stiffness element possessing axial and rotational degrees-of-freedom. The rotating testing demonstrated load capability of 1816 lbs (8.1 kN), which equates to a thrust bearing unit load of 67 psi (0.46 MPa). Gas film force–deflection curves reveal a nonlinear relationship between thrust load and film clearance. Comparison of film thickness values with the predictive model show good agreement under high load and inlet pressure, however deviate as load and pressure decrease. Load capability was shown to increase with increasing hydrostatic inlet pressure, while the increase in thrust runner speed revealed a small decrease in load capacity.

Engineering↗
DOE OSTI2024

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗