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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Modification of ion-temperature-gradient turbulence by impurities in stellarator plasmas

Recent nonlinear gyrokinetic simulations have shown that impurities can strongly modify the turbulent heat flux in stellarator plasmas. Here, the ion-temperature-gradient (ITG) dispersion relation in a plasma containing impurities is analytically solved in certain limits and an expression for the modification of the ITG growth rate by impurities is derived. The analytical expression is the sum of three terms corresponding to three different physical causes (impurity density gradient, impurity temperature gradient and dilution) of the change in the growth rate. The scalings predicted analytically for the modification of the growth rate are shown to be reproduced by linear gyrokinetic simulations. The conditions for reduction or increase of the ITG growth by impurities are also correctly predicted by the analytical solution to the dispersion relation. Finally, a remarkable correlation is found between the analytical expression for the modification of the growth rate and the modification of the turbulent heat flux obtained from nonlinear gyrokinetic simulations.

Calvo, Iván [Research Centre for Energy, Environme↗
DOE OSTI2016

Azimuthal decorrelation of jets widely separated in rapidity in pp collisions at $\sqrt{s} =$ 7 TeV

The decorrelation in the azimuthal angle between the most forward and the most backward jets (Mueller-Navelet jets) is measured in data collected in pp collisions with the CMS detector at the LHC at $\sqrt{s} =$ 7 TeV. The measurement is presented in the form of distributions of azimuthal-angle differences, $\Delta\phi$, between the Mueller-Navelet jets, the average cosines of $(\pi-\Delta\phi)$, $2(\pi-\Delta\phi)$, and $3(\pi-\Delta\phi)$, and ratios of these cosines. The jets are required to have transverse momenta, $p_{\mathrm{T}}$, in excess of 35 GeV and rapidities, $| y |$, of less than 4.7. The results are presented as a function of the rapidity separation, $\Delta{y}$, between the Mueller-Navelet jets, reaching $\Delta{y}$ up to 9.4 for the first time. Lastly, the results are compared to predictions of various Monte Carlo event generators and to analytical predictions based on the DGLAP and BFKL parton evolution schemes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Tandem Predictions for HPC Jobs

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

HPC↗
DOE OSTI2025

Tandem Predictions for HPC Jobs: Preprint

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Driven resonant current amplification in self-organized plasma configurations with uniform λ and plasma pressure confinement

Driven resonant current amplification in uniform λ=$^{μoJ}_{B}$, self-organized plasma configurations with plasma pressure confinement is studied. This work extends previous analyses of force-free states (J ∥ B) to equilibria with plasma pressure gradients (∇P = J × B ≠ 0). An analytical treatment shows that in equilibria with uniform λ and a plasma pressure gradient that varies linearly with poloidal magnetic flux, driven resonances are either downshifted or upshifted from their force-free values. Additionally, a modification of off resonance current amplification is observed in pressure confining equilibria. Computational results add support to analytical predictions, and also include driven resonance behavior with equilibrium profiles not considered analytically. Furthermore, these results suggest not only persistence, but also modification of driven resonant current amplification in sustained spheromak configurations with plasma pressure confinement when compared to force-free states. By providing a basis for the existence of driven, high current amplification, pressure confining equilibria, this work provides insight into the accessibility of sustained spheromak states of most interest for potential fusion energy applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

SU-E-T-630: Predictive Modeling of Mortality, Tumor Control, and Normal Tissue Complications After Stereotactic Body Radiotherapy for Stage I Non-Small Cell Lung Cancer

Purpose: While rates of local control have been well characterized after stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC), less data are available characterizing survival and normal tissue toxicities, and no validated models exist assessing these parameters after SBRT. We evaluate the reliability of various machine learning techniques when applied to radiation oncology datasets to create predictive models of mortality, tumor control, and normal tissue complications. Methods: A dataset of 204 consecutive patients with stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT) at the University of Pennsylvania between 2009 and 2013 was used to create predictive models of tumor control, normal tissue complications, and mortality in this IRB-approved study. Nearly 200 data fields of detailed patient- and tumor-specific information, radiotherapy dosimetric measurements, and clinical outcomes data were collected. Predictive models were created for local tumor control, 1- and 3-year overall survival, and nodal failure using 60% of the data (leaving the remainder as a test set). After applying feature selection and dimensionality reduction, nonlinear support vector classification was applied to the resulting features. Models were evaluated for accuracy and area under ROC curve on the 81-patient test set. Results: Models for common events in the dataset (such as mortality at one year) had the highest predictive power (AUC = .67, p < 0.05). For rare occurrences such as radiation pneumonitis and local failure (each occurring in less than 10% of patients), too few events were present to create reliable models. Conclusion: Although this study demonstrates the validity of predictive analytics using information extracted from patient medical records and can most reliably predict for survival after SBRT, larger sample sizes are needed to develop predictive models for normal tissue toxicities and more advanced machine learning methodologies need be consider in the future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

EVOLUTION OF GALAXY GROUPS IN THE ILLUSTRIS SIMULATION

We present the first study of the evolution of galaxy groups in the Illustris simulation. We focus on dynamically relaxed and unrelaxed galaxy groups representing dynamically evolved and evolving galaxy systems, respectively. The evolutionary state of a group is probed from its luminosity gap and separation between the brightest group galaxy and the center of mass of the group members. We find that the Illustris simulation overproduces galaxy systems with a large luminosity gap, known as fossil systems, in comparison to observations and the probed semi-analytical predictions. However, this simulation is just as successful as the probed semi-analytic model in recovering the correlation between luminosity gap and offset of the luminosity centroid. We find evolutionary tracks based on luminosity gap that indicate that a group with a large luminosity gap is rooted in one with a small luminosity gap, regardless of the position of the brightest group galaxy within the halo. This simulation helps to explore, for the first time, the black hole mass and its accretion rate in galaxy groups. For a given stellar mass of the brightest group galaxies, the black hole mass is larger in dynamically relaxed groups with a lower rate of mass accretion. We find this to be consistent with the latest observational studies of radio activity in the brightest group galaxies in fossil groups. We also find that the intragalactic medium in dynamically evolved groups is hotter for a given halo mass than that in evolving groups, again consistent with earlier observational studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2015

Inductively Driven, 3D Liner Compression of a Magnetized Plasma to Megabar Energy Densities

To take advantage of the smaller scale, higher density regime of fusion an efficient method for achieving the compressional heating required to reach fusion gain conditions must be found. What is proposed is a more flexible metallic liner compression scheme that minimizes the kinetic energy required to reach fusion. It is believed that it is possible to accomplish this at sub-megajoule energies. This however will require operation at very small scale. To have a realistic hope of inexpensive, repetitive operation, it is essential to have the liner kinetic energy under a megajoule which allows for the survivability of the vacuum and power systems. At small scale the implosion speed must be reasonably fast to maintain the magnetized plasma (FRC) equilibrium during compression. For limited liner kinetic energy, it becomes clear that the thinnest liner imploded to the smallest radius consistent with the requirements for FRC equilibrium lifetime is desired. The proposed work is directed toward accomplishing this goal. Typically an axial (Z) current is employed for liner compression. There are however several advantages to using a θ-pinch coil. With the θ-pinch the liner currents are inductively driven which greatly simplifies the apparatus and vacuum system, and avoids difficulties with the post implosion vacuum integrity. With fractional flux leakage, the foil liner automatically provides for the seed axial compression field. To achieve it with optimal switching techniques, and at an accelerated pace however will require additional funding. This extra expense is well justified as the compression technique that will be enabled by this funding is unique in the ability to implode individual segments of the liner at different times. This is highly advantageous as the liner can be imploded in a manner that maximizes the energy transfer to the FRC. Production of shaped liner implosions for additional axial compression can thus be readily accomplished with the modified power modules. The additional energy and switching capability proposed will thus provide for optimal utilization of the liner energy. The following tasks were outlined for the three year effort: (1) Design and assemble the foil liner compression test structure and chamber including the compression bank and test foils [Year 1]. (2) Perform foil liner compression experiments and obtain performance data over a range on liner dimensions and bank parameters [Year 2]. (3) Carry out compression experiments of the FRC plasma to Megagauss fields and measure key fusion parameters [Year 3]. (4) Develop numerical codes and analyze experimental results, and determine the physics and scaling for future work [Year 1-3]. The principle task of the project was to design and assemble the foil liner FRC formation chamber, the full compression test structure and chamber including the compression bank. This task was completed successfully. The second task was to test foils in the test facility constructed in year one and characterize the performance obtained from liner compression. These experimental measurements were then compared with analytical predictions, and numerical code results. The liner testing was completed and compared with both the analytical results as well as the code work performed with the 3D structural dynamics package of ANSYS Metaphysics®. This code is capable of modeling the dynamic behavior of materials well into the non-linear regime (e.g. a bullet hit plate glass). The liner dynamic behavior was found to be remarkably close to that predicted by the 3D structural dynamics results. Incorporating a code that can also include the magnetics and plasma physics has also made significant progress at the UW. The remaining test bed construction and assembly task is was completed, and the FRC formation and merging experiments were carried out as planned. The liner compression of the FRC to Megagauss fields was not performed due to not obtaining a sufficiently long lived FRC during the final year of the grant. Modifications planned to correct this deficiency included a larger FRC source as well as a much larger liner driver energy storage system. Due to discontinuation of the grant neither of these improvements were carried out.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

On the replica structure of Sachdev-Ye-Kitaev model

We investigate existence of replica off-diagonal solutions in the field-theoretical description of Sachdev-Ye-Kitaev model. To this end we evaluate a set of local and non-local dynamic correlation functions in the long time limit. We argue that the structure of the soft-mode Schwarzian action is qualitatively different in replica-diagonal vs. replica-off-diagonal scenarios, leading to distinct long-time predictions for the correlation functions. We then evaluate the corresponding correlation functions numerically and compare the simulations with analytical predictions of replica-diagonal and replica-off-diagonal calculations. We conclude that all our numerical results are in a quantitative agreement with the theory based on the replica-diagonal saddle point plus Schwarzian and massive Gaussian fluctuations (the latter do contain replica off-diagonal components). This seems to exclude any contributions from replica-off-diagonal saddle points, at least on the time scales shorter than the inverse many-body level spacing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Blockchain-based decentralized computing

An exemplary blockchain-based decentralized computing system and method are disclosed for industrial analytics applications. The exemplary system and method leverage blockchain technology to deliver and execute privacy-preserving decentralized predictive analytics, machine learning, and optimization operations for various industrial applications using a set of self-contained analytics block smart contracts that can be readily utilized and in analytics applications to deploy across multiple sites.

Ramanan, Paritosh P.↗
DOE OSTI2017

Particle simulation of radio frequency waves with fully-kinetic ions and gyrokinetic electrons

A toroidal particle-in-cell (PIC) code, suitable for investigating nonlinear phenomena in radio frequency (RF) heating and current drive, is developed and verified thereafter through a series of fidelity tests for field solvers and single particle motions in toroidal geometry, where simulation results show good coincidence with analytical prediction. The RF capability is then demonstrated through the integrated benchmarks with linear lower hybrid wave and ion Bernstein wave theory in cylindrical geometry, where the analytic result is easily available. The frequency and mode structure in the simulations agree well with the theoretical prediction.

Lin, Jingbo↗
DOE OSTI2017

Pressure Self-focusing Effect and Novel Methods for Increasing the Maximum Pressure in Traditional and Rotational Diamond Anvil Cells

The main principles of producing a region near the center of a sample, compressed in a diamond anvil cell (DAC), with a very high pressure gradient and, consequently, with high pressure are predicted theoretically. The revealed phenomenon of generating extremely high pressure gradient is called the pressure self-focusing effect. Initial analytical predictions utilized generalization of a simplified equilibrium equation. Then, the results are refined using our recent advanced model for elastoplastic material under high pressures in finite element method (FEM) simulations. The main points in producing the pressure self-focusing effect are to use beveled anvils and reach a very thin sample thickness at the center. We find that the superposition of torsion in a rotational DAC (RDAC) offers drastic enhancement of the pressure self-focusing effect and allows one to reach the same pressure under a much lower force and deformation of anvils.

42 ENGINEERING↗
DOE OSTI2016

Friction behavior of a multi-interface system and improved performance by AlMgB 14 –TiB 2 –C and diamond-like-carbon coatings

We investigated friction behavior of a bearing system with two interfaces involved: a roller component experiencing rolling–sliding interaction against twin cylinders under point contacts while simultaneously undergoing pure sliding interaction against a socket under a conformal contact. Lubrication modeling predicted a strong correlation between the roller's rolling condition and the system's friction behavior. Experimental observations first validated the analytical predictions using steel and iron components. Diamond-like-carbon (DLC) coating and AlMgB 14 –TiB 2 coating with a carbon topcoat (BAMC) were then applied to the roller and twin cylinders, respectively. In conclusion, testing and analysis results suggest that the coatings effectively decreased the slip ratio for the roller–cylinder contact and the sliding friction at both bearing interfaces and, as a result, significantly reduced the system frictional torque.

42 ENGINEERING↗
DOE OSTI2015

Transient and chaotic low-energy transfers in a system with bistable nonlinearity

The low-energy dynamics of a two-dof system composed of a grounded linear oscillator coupled to a lightweight mass by means of a spring with both cubic nonlinear and negative linear components is investigated. The mechanisms leading to intense energy exchanges between the linear oscillator, excited by a low-energy impulse, and the nonlinear attachment are addressed. For lightly damped systems, it is shown that two main mechanisms arise: Aperiodic alternating in-well and cross-well oscillations of the nonlinear attachment, and secondary nonlinear beats occurring once the dynamics evolves solely in-well. The description of the former dissipative phenomenon is provided in a two-dimensional projection of the phase space, where transitions between in-well and cross-well oscillations are associated with sequences of crossings across a pseudo-separatrix. Whereas the second mechanism is described in terms of secondary limiting phase trajectories of the nonlinear attachment under certain resonance conditions. The analytical treatment of the two aformentioned low-energy transfer mechanisms relies on the reduction of the nonlinear dynamics and consequent analysis of the reduced dynamics by asymptotic techniques. Direct numerical simulations fully validate our analytical predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Polarization whorls from M87* at the event horizon telescope

The event horizon telescope (EHT) is anticipated to soon produce polarimetric images of the supermassive black hole at the centre of the neighbouring galaxy M87. There are indications that this black hole is rapidly spinning. General relativity predicts that such a high-spin black hole has an emergent conformal symmetry near its event horizon. In this paper, we use this symmetry to analytically predict the polarized near-horizon emissions to be seen at the EHT and find a distinctive pattern of whorls aligned with the spin.

79 ASTRONOMY AND ASTROPHYSICS↗