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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

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

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

An analysis of the 70-meter antenna hydrostatic bearing by means of computer simulation

Recently, the computer program 'A Computer Solution for Hydrostatic Bearings with Variable Film Thickness,' used to design the hydrostatic bearing of the 70-meter antennas, was modified to improve the accuracy with which the program predicts the film height profile and oil pressure distribution between the hydrostatic bearing pad and the runner. This article presents a description of the modified computer program, the theory upon which the computer program computations are based, computer simulation results, and a discussion of the computer simulation results.

Bartos, R. D.↗
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 OSTI2017

A Computational Simulation of Using Tungsten Gratings in Near-Field Thermophotovoltaic Devices

Near-field thermophotovoltaic (NFTPV) devices have received much attention lately as an alternative energy harvesting system, whereby a heated emitter exchanges super-Planckian thermal radiation with a photovoltaic (PV) cell to generate electricity. This work describes the use of a grating structure to enhance the power throughput of NFTPV devices, while increasing the energy conversion efficiency by ensuring that a large portion of the radiation entering the PV cell is above the band gap. The device contains a high-temperature tungsten grating that radiates photons to a room-temperature In 0.18 Ga 0.82 Sb PV cell through a vacuum gap of several tens of nanometers. Scattering theory is used along with the rigorous coupled-wave analysis (RCWA) to calculate the radiation energy exchange between the grating emitter and the TPV cell. A parametric study is performed by varying the grating depth, period, and ridge width in the range that can be fabricated using available fabrication technologies. It is found that the power output can be increased by 40% while improving the efficiency from 29.9% to 32.0% with a selected grating emitter as compared to the case of a flat tungsten emitter. Reasons for the enhancement are found to be due to the enhanced energy transmission coefficient close to the band gap. Furthermore, this work shows a possible way of improving NFTPV and sheds light on how grating structures interact with thermal radiation at the nanoscale.

42 ENGINEERING↗
NASA NTRS1978

Evaluation of Rankine cycle air conditioning system hardware by computer simulation

A computer program for simulating the performance of a variety of solar powered Rankine cycle air conditioning system components (RCACS) has been developed. The computer program models actual equipment by developing performance maps from manufacturers data and is capable of simulating off-design operation of the RCACS components. The program designed to be a subroutine of the Marshall Space Flight Center (MSFC) Solar Energy System Analysis Computer Program 'SOLRAD', is a complete package suitable for use by an occasional computer user in developing performance maps of heating, ventilation and air conditioning components.

Healey, H. M.↗
DOE OSTI2016

Density-functional theory computer simulations of CZTS{sub 0.25}Se{sub 0.75} alloy phase diagrams

Density-functional theory simulations of CZTS, CZTSe, and CZTS{sub 0.25}Se{sub 0.75} photovoltaic compounds have been performed to investigate the stability of the CZTS{sub 0.25}Se{sub 0.75} alloy vs. decomposition into CZTS, CZTSe, and other secondary compounds. The Gibbs energy for vibrational contributions was estimated by calculating phonon spectra and thermodynamic properties at finite temperatures. It was demonstrated that the CZTS{sub 0.25}Se{sub 0.75} alloy is stabilized not by enthalpy of formation but primarily by the mixing contributions to the Gibbs energy. The Gibbs energy gains/losses for several decomposition reactions were calculated as a function of temperature with/without intermixing and vibration contributions to the Gibbs energy. A set of phase diagrams was built in the multidimensional space of chemical potentials at 300 K and 900 K temperatures to demonstrate alloy stability and boundary compounds at various chemical conditions. It demonstrated for CZTS{sub 0.25}Se{sub 0.75} that the chemical potentials for stability differ between typical processing temperature (∼900 K) and operating temperature (300 K). This implies that as cooling progresses, the flux/concentration of S should be increased in MBE growth to maintain the CZTS{sub 0.25}Se{sub 0.75} in a thermodynamically stable state to minimize phase decomposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Computational simulations of hydrogen circular migration in protonated acetylene induced by circularly polarized light

The hydrogens in protonated acetylene are very mobile and can easily migrate around the C 2 core by moving between classical and non-classical structures of the cation. The lowest energy structure is the T-shaped, non-classical cation with a hydrogen bridging the two carbons. Conversion to the classical H 2 CCH + ion requires only 4 kcal/mol. The effect of circularly polarized light on the migration of hydrogens in oriented C 2 H 3 + has been simulated by Born-Oppenheimer molecular dynamics. Classical trajectory calculations were carried out with the M062X/6-311+G(3df,2pd) level of theory using linearly and circularly polarized 32 cycle 7 μm cosine squared pulses with peak intensity of 5.6 × 10 13 W/cm 2 and 3.15 × 10 13 W/cm 2 , respectively. These linearly and circularly polarized pulses transfer similar amounts of energy and total angular momentum to C 2 H3 + . The average angular momentum vectors of the three hydrogens show opposite directions of rotation for right and left circularly polarized light, but no directional preference for linearly polarized light. This difference results in an appreciable amount of angular displacement of the three hydrogens relative to the C 2 core for circularly polarized light, but only an insignificant amount for linearly polarized light. In conclusion, over the course of the simulation with circularly polarized light, this corresponds to a propeller-like motion of the three hydrogens around the C 2 core of protonated acetylene.

08 HYDROGEN↗
NASA NTRS1990

Composite structure global fracture toughness via computational simulation

A computational method for the simulation of damage and fracture propagation in laminated composites is presented. A quantitative evaluation of the global fracture toughness of composites is shown as a tool for monitoring the fracture stability of composites under sustained loading. Changes in overall structural properties such as natural frequencies and the fundamental buckling load are also computed with increasing load-induced damage. Structural degradation, delamination, fracture, and damage propagation are included in the simulation. An angle-plied composite plate structure subjected to inplane tensile loading is used as an example to demonstrate some of the feature of the computational method.

Minnetyan, L.↗
DOE OSTI2019

Computational simulation of a 100 kW dual circulating fluidized bed reactor processing coal by chemical looping with oxygen uncoupling

Chemical looping with oxygen uncoupling (CLOU) is a promising carbon capture technology that utilizes two interconnected fluidized-bed reactors to separate oxygen from air using a metal oxide, and then to use that oxygen to combust fuel in a nitrogen-free environment. CLOU is particularly well suited for solid fuels such as coal since released O 2 will readily combust with solid char. This study presents simulations of the University of Utah's dual fluidized bed chemical looping process development unit (PDU) operating as a CLOU system at 100 kW th , using CPFD Software's Barracuda-VR™ CFD modeling program. A full 3D model of the PDU was used including both reactors, as well as loop seals and cyclone separators. Hydrodynamic settings and the drag model are based on previous cold-flow validation experiments. Mechanisms describing CLOU reaction kinetics for cuprous oxide oxidation in the air reactor and cupric oxide reduction in the fuel reactor were adapted from previously completed experimental work. Coal char kinetics were based on the carbon-hydrogen-nitrogen-oxygen-sulfur, CHNOS, method. Submodels for devolatilization, gasification, and homogenous gas-phase reactions used relationships from the literature. Results predict volatile burnout almost immediately, though coal combustion continues into the upper portions of the fuel reactor and cyclone. Lastly, nitrogen gas leakage into the fuel reactor is shown to be mostly carryover from the air reactor

42 ENGINEERING↗
NASA NTRS1979

Interpretive computer simulator for the NASA Standard Spacecraft Computer-2 (NSSC-2)

An Interpretive Computer Simulator (ICS) for the NASA Standard Spacecraft Computer-II (NSSC-II) was developed as a code verification and testing tool for the Annular Suspension and Pointing System (ASPS) project. The simulator is written in the higher level language PASCAL and implented on the CDC CYBER series computer system. It is supported by a metal assembler, a linkage loader for the NSSC-II, and a utility library to meet the application requirements. The architectural design of the NSSC-II is that of an IBM System/360 (S/360) and supports all but four instructions of the S/360 standard instruction set. The structural design of the ICS is described with emphasis on the design differences between it and the NSSC-II hardware. The program flow is diagrammed, with the function of each procedure being defined; the instruction implementation is discussed in broad terms; and the instruction timings used in the ICS are listed. An example of the steps required to process an assembly level language program on the ICS is included. The example illustrates the control cards necessary to assemble, load, and execute assembly language code; the sample program to to be executed; the executable load module produced by the loader; and the resulting output produced by the ICS.

Smith, R. S.↗
DOE OSTI2019

Progress Report on An Exascale Computational Simulation Capability for Pervasive Fracture & Failure

How does a structure fail in an explosion? Is there an engineering design opportunity to mitigate the effects of a blast? These are questions that may be addressed with simulation; however, a computational simulation capability to answer these questions must include developments in material physics modeling and numerical algorithms that we believe are possible, but do not yet exist. This LDRD project proposes to develop the physics models and computational approaches to create a computational simulation approach to answer these physics and engineering questions.

42 ENGINEERING↗
NASA NTRS1973

Computer simulation of space station computer steered high gain antenna

The mathematical modeling and programming of a complete simulation program for a space station computer-steered high gain antenna are described. The program provides for reading input data cards, numerically integrating up to 50 first order differential equations, and monitoring up to 48 variables on printed output and on plots. The program system consists of a high gain antenna, an antenna gimbal control system, an on board computer, and the environment in which all are to operate.

Beach, S. W.↗
NASA NTRS1995

Launch Site Computer Simulation and its Application to Processes

This paper provides an overview of computer simulation, the Lockheed developed STS Processing Model, and the application of computer simulation to a wide range of processes. The STS Processing Model is an icon driven model that uses commercial off the shelf software and a Macintosh personal computer. While it usually takes one year to process and launch 8 space shuttles, with the STS Processing Model this process is computer simulated in about 5 minutes. Facilities, orbiters, or ground support equipment can be added or deleted and the impact on launch rate, facility utilization, or other factors measured as desired. This same computer simulation technology can be used to simulate manufacturing, engineering, commercial, or business processes. The technology does not require an 'army' of software engineers to develop and operate, but instead can be used by the layman with only a minimal amount of training. Instead of making changes to a process and realizing the results after the fact, with computer simulation, changes can be made and processes perfected before they are implemented.

Sham, Michael D.↗
DOE OSTI2021

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

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
NASA NTRS2006

Composite Erosion by Computational Simulation

Composite degradation is evaluated by computational simulation when the erosion degradation occurs on a ply-by-ply basis and the degrading medium (device) is normal to the ply. The computational simulation is performed by a multi factor interaction model and by a multi scale and multi physics available computer code. The erosion process degrades both the fiber and the matrix simultaneously in the same slice (ply). Both the fiber volume ratio and the matrix volume ratio approach zero while the void volume ratio increases as the ply degrades. The multi factor interaction model simulates the erosion degradation, provided that the exponents and factor ratios are selected judiciously. Results obtained by the computational composite mechanics show that most composite characterization properties degrade monotonically and approach "zero" as the ply degrades completely.

Chamis, Christos C.↗
NASA NTRS1990

Fiber pushout test: A three-dimensional finite element computational simulation

A fiber pushthrough process was computationally simulated using three-dimensional finite element method. The interface material is replaced by an anisotropic material with greatly reduced shear modulus in order to simulate the fiber pushthrough process using a linear analysis. Such a procedure is easily implemented and is computationally very effective. It can be used to predict fiber pushthrough load for a composite system at any temperature. The average interface shear strength obtained from pushthrough load can easily be separated into its two components: one that comes from frictional stresses and the other that comes from chemical adhesion between fiber and the matrix and mechanical interlocking that develops due to shrinkage of the composite because of phase change during the processing. Step-by-step procedures are described to perform the computational simulation, to establish bounds on interfacial bond strength and to interpret interfacial bond quality.

Mital, Subodh K.↗