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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The paradox of the tight spiral pass in American football: A simple resolution

An American football is a rotationally symmetric object, which, when well-thrown, spins rapidly around its symmetry axis. In the absence of aerodynamic effects, the football would be a torque-free gyroscope and the symmetry/spin axis would remain pointing in a fixed direction in space as the football moved on its parabolic path. When a pass is well-thrown through the atmosphere, however, the symmetry axis remains—at least approximately—tangent to the path of motion. The rotation of the symmetry axis must be due to aerodynamic torque; yet, that torque, at first glance, would seem to have precisely the opposite effect. In this work, we explain the action of aerodynamics on the ball's orientation at second glance.

42 ENGINEERING↗
DOE OSTI2021

Multiphysics coupling in the Exascale computing project

Multiphysics coupling presents a significant challenge in terms of both computational accuracy and performance. Achieving high performance on coupled simulations can be particularly challenging in a high-performance computing context. The US Department of Energy Exascale Computing Project has the mission to prepare mission-relevant applications for the delivery of the exascale computers starting in 2023. Many of these applications require multiphysics coupling, and the implementations must be performant on exascale hardware. In this special issue we feature six articles performing advanced multiphysics coupling that span the computational science domains in the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Chemical Physics (physics.chem-ph)↗
NASA NTRS1974

PLOTTER: An independent computer program for the generation of graphical displays

A computer program is described for generating graphical information from input data or auxiliary analysis programs on a variety of graphical devices. Options are presented for tabulating the data in columnar format and for plotting auxiliary text in the vicinity of the plotted information. Display device selection is accomplished by interfacing the basic computer code through routines which convert the internally generated plot vectors to hardware commands for the display device. The plotting techniques employed in the computer program are discussed. User's instructions are presented with examples which illustrate the use of the program in generating plotted information from various sources and presenting the information in alternate plot formats. Technical discussion of the computer code is presented giving the physical characteristics, computer loading instructions and descriptions of the subroutines.

Glatt, C. R.↗
NASA NTRS1971

Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

Drake, R. L.↗
DOE OSTI2023

MAS: A versatile Landau-fluid eigenvalue code for plasma stability analysis in general geometry

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number (n) and frequency (ω) in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field (E||), ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfv\'en wave (KAW), ion sound wave (ISW), low-n kink, high-n ion temperature gradient mode (ITG) and kinetic ballooning mode (KBM). Moreover, MAS is successfully applied to model the Alfv\'en eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of reversed shear Alfv\'en eigenmode (RSAE), the tunneling damping of toroidal Alfv\'en eigenmode (TAE), as well as the polarization characteristics of kinetic beta-induced Alfv\'en eigenmode (KBAE) and beta-induced Alfv\'en-acoustic eigenmode (BAAE) being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. Additionally, we also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A circuit-generated quantum subspace algorithm for the variational quantum eigensolver

Recent research has shown that wavefunction evolution in real and imaginary time can generate quantum subspaces with significant utility for obtaining accurate ground state energies. Inspired by these methods, we propose combining quantum subspace techniques with the variational quantum eigensolver (VQE). In our approach, the parameterized quantum circuit is divided into a series of smaller subcircuits. The sequential application of these subcircuits to an initial state generates a set of wavefunctions that we use as a quantum subspace to obtain high-accuracy groundstate energies. We call this technique the circuit subspace variational quantum eigensolver (CSVQE) algorithm. By benchmarking CSVQE on a range of quantum chemistry problems, we show that it can achieve significant error reduction in the best case compared to conventional VQE, particularly for poorly optimized circuits, greatly improving convergence rates. Furthermore, we demonstrate that when applied to circuits trapped at local minima, CSVQE can produce energies close to the global minimum of the energy landscape, making it a potentially powerful tool for diagnosing local minima.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Reconstructing thermal quantum quench dynamics from pure states

Simulating the nonequilibrium dynamics of thermal states is a fundamental problem across scales from high-energy to condensed-matter physics. Quantum computers may provide a way to solve this problem efficiently. Preparing a thermal state on a quantum computer is challenging, but there exist methods to circumvent this by computing a weighted sum of time-dependent matrix elements in a convenient basis. Further, while the number of basis states can be large, in this paper we show that it can be reduced by simulating only the largest density matrix elements by weight, capturing the density matrix to a specified precision. Leveraging Hamiltonian symmetries enables further reductions. This approach paves the way to more accurate thermal-state dynamics simulations on near-term quantum hardware.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Energy and physical resource impacts of quantum computing merit greater attention

Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational framework for modeling of physical process

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

Chen, Xiao↗
DOE OSTI2024

Computational framework for modeling of physical process

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

Chen, Xiao↗
NASA NTRS2014

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Clevleand, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSLthe first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Ice Crystal Icing↗
NASA NTRS2014

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Cleveland, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSL--the first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Turbofan Engine↗
DOE OSTI2024

Chiral limit of 2d QCD revisited with lightcone conformal truncation

We study the chiral limit of 2d QCD with a single quark flavor at finite Nc using LCT. By modifying the LCT basis according to the quark mass in a manner motivated by ’t Hooft’s analysis, we are able to restore convergence for quark masses much smaller than the QCD strong coupling scale. For such small quark masses, the IR of the theory is expected to be well described by the Sine-Gordon model. We verify that LCT numerics are able to capture in detail the spectrum and correlation functions of the Sine-Gordon model. This opens up the possibility for studying deformations of various integrable CFTs using LCT by considering the chiral limit of QCD like theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗