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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Analyzing Robotic Kinematics Via Computed Simulations

Computing system assists in evaluation of kinematics of conceptual robot. Displays positions and motions of robotic manipulator within work cell. Also displays interactions between robotic manipulator and other objects. Results of simulation displayed on graphical computer workstation. System includes both off-the-shelf software originally developed for automotive industry and specially developed software. Simulation system also used to design human-equivalent hand, to model optical train in infrared system, and to develop graphical interface for teleoperator simulation system.

Carnahan, Timothy M.↗
NASA NTRS1986

Composite sandwich thermostructural behavior - Computational simulation

Computational methods have been developed for simulating the thermomechanical behavior of composite sandwiches, in which the analyses with several levels of progressive sophistication were used in conjunction with composite hygrothermomechanical theory. The sophistication levels include: (1) three-dimensional detailed finite element modeling of the honeycomb, the adhesive, and the composite faces; (2) three-dimensional finite element modeling assuming a homogeneous core; (3) laminate theory simulation; and (4) simple equations for predicting the equivalent properties of the honeycomb core. These levels have been packaged into a procedure embedded in a computer code streamlined for the simulation of the composite sandwich hygrothermal and structural behavior. It is shown that in order to properly simulate the thermomechanical response of the composite sandwich, all the honeycomb thermal and mechanical properties must be used.

Chamis, C. C.↗
NASA NTRS1990

Hypersonic flow - Physical issues and computational simulation

Computational results are presented on hypersonic flows, with emphasis on thermal and chemical nonequilibrium effects and the role played by grids. The continuum equations and their boundary conditions are presented, and the direct simulation Monte Carlo method is described. The importance of using the correct computational and physical models in describing hypersonic nonequilibrium flows is demonstrated.

Hassan, H. A.↗
NASA NTRS2000

Computer Simulations of Small Molecules in Membranes: Insights from Computer Simulations into the Interactions of Small Molecules with Lipid Bilayers

Two of Ernest Overton's lasting contributions to biology are the Meyer-Overton relationship between the potency of an anesthetic and its solubility in oil, and the Overton rule which relates the permeability of a membrane to the oil-water partition coefficient of the permeating molecule. A growing body of experimental evidence, however, cannot be reconciled with these theories. In particular, the molecular nature of membranes, unknown to Overton, needs to be included in any description of these phenomena. Computer simulations are ideally suited for providing atomic-level information about the behavior of small molecules in membranes. The authors discuss simulation studies relevant to Overton's ideas. Through simulations it was found that anesthetics tend to concentrate at interfaces and their anesthetic potency correlates better with solubility at the water-membrane interface than with solubility in oil. Simulation studies of membrane permeation revealed the anisotropic nature of the membranes, as evidenced, for example, by the highly nonuniform distribution of free volume in the bilayer. This, in turn, influences the diffusion rates of solutes, which increase with the depth in the membrane. Small solutes tend to move by hopping between voids in the bilayer, and this hopping motion may be responsible for the deviation from the Overton rule of the permeation rates of these molecules.

Pohorille, Andrew↗
NASA NTRS2007

Polymer Composites Corrosive Degradation: A Computational Simulation

A computational simulation of polymer composites corrosive durability is presented. The corrosive environment is assumed to manage the polymer composite degradation on a ply-by-ply basis. The degradation is correlated with a measured pH factor and is represented by voids, temperature and moisture which vary parabolically for voids and linearly for temperature and moisture through the laminate thickness. The simulation is performed by a computational composite mechanics computer code which includes micro, macro, combined stress failure and laminate theories. This accounts for starting the simulation from constitutive material properties and up to the laminate scale which exposes the laminate to the corrosive environment. Results obtained for one laminate indicate that the ply-by-ply degradation degrades the laminate to the last one or the last several plies. Results also demonstrate that the simulation is applicable to other polymer composite systems as well.

Chamis, Christos C.↗
NASA NTRS1986

Fiber Composite Sandwich Thermostructural Behavior: Computational Simulation

Several computational levels of progressive sophistication/simplification are described to computationally simulate composite sandwich hygral, thermal, and structural behavior. The computational levels of sophistication include: (1) three-dimensional detailed finite element modeling of the honeycomb, the adhesive and the composite faces; (2) three-dimensional finite element modeling of the honeycomb assumed to be an equivalent continuous, homogeneous medium, the adhesive and the composite faces; (3) laminate theory simulation where the honeycomb (metal or composite) is assumed to consist of plies with equivalent properties; and (4) derivations of approximate, simplified equations for thermal and mechanical properties by simulating the honeycomb as an equivalent homogeneous medium. The approximate equations are combined with composite hygrothermomechanical and laminate theories to provide a simple and effective computational procedure for simulating the thermomechanical/thermostructural behavior of fiber composite sandwich structures.

Chamis, C. C.↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Fiber Composite Sandwich Thermostructural Behavior - Computational Simulation

Four computational simulation methods with different levels of sophistication were used to simulate thermal behavior and structural changes of composite sandwich panels with a honeycomb core subjected to a variety of environmental effects. The models on thich these methods are based include three-dimensional finite-element modeling, three-dimensional finite-element modeling assuming a homogeneous core, laminate theory, and simple equations for predicting the equivalent properties of the honeycomb core. A procedure was developed and embedded in a composite mechanics computer code, which made it possile to conduct parametric studies to determine 'optimum' composite sandwich configurations for specific applications. The procedure was applied for the evaluation of composite sandwich behavior at the global, local, laminate, ply, and micromechanics levels when the composite sandwich is subjected to hygral, thermal, and mechanical loading environments.

Chamis, Christos C.↗
NASA NTRS1974

Gate-controlled-diodes in silicon-on-sapphire: A computer simulation

The computer simulation of the electrical behavior of a Gate-Controlled Diode (GCD) fabricated in Silicon-On-Sapphire (SOS) was described. A procedure for determining lifetime profiles from capacitance and reverse current measurements on the GCD was established. Chapter 1 discusses the SOS structure and points out the need of lifetime profiles to assist in device design for GCD's and bipolar transistors. Chapter 2 presents the one-dimensional analytical formula for electrostatic analysis of the SOS-GCD which are useful for data interpretation and setting boundary conditions on a simplified two-dimensional analysis. Chapter 3 gives the results of a two-dimensional analysis which treats the field as one-dimensional until the silicon film is depleted and the field penetrates the sapphire substrate. Chapter 4 describes a more complete two-dimensional model and gives results of programs implementing the model.

Gassaway, J. D.↗
NASA NTRS1989

Computational structural mechanics engine structures computational simulator

The Computational Structural Mechanics (CSM) program at Lewis encompasses: (1) fundamental aspects for formulating and solving structural mechanics problems, and (2) development of integrated software systems to computationally simulate the performance/durability/life of engine structures.

Chamis, C. C.↗
NASA NTRS1990

Incorporation of shuttle CCT parameters in computer simulation models

Computer simulations of shuttle missions have become increasingly important during recent years. The complexity of mission planning for satellite launch and repair operations which usually involve EVA has led to the need for accurate visibility and access studies. The PLAID modeling package used in the Man-Systems Division at Johnson currently has the necessary capabilities for such studies. In addition, the modeling package is used for spatial location and orientation of shuttle components for film overlay studies such as the current investigation of the hydrogen leaks found in the shuttle flight. However, there are a number of differences between the simulation studies and actual mission viewing. These include image blur caused by the finite resolution of the CCT monitors in the shuttle and signal noise from the video tubes of the cameras. During the course of this investigation the shuttle CCT camera and monitor parameters are incorporated into the existing PLAID framework. These parameters are specific for certain camera/lens combinations and the SNR characteristics of these combinations are included in the noise models. The monitor resolution is incorporated using a Gaussian spread function such as that found in the screen phosphors in the shuttle monitors. Another difference between the traditional PLAID generated images and actual mission viewing lies in the lack of shadows and reflections of light from surfaces. Ray tracing of the scene explicitly includes the lighting and material characteristics of surfaces. The results of some preliminary studies using ray tracing techniques for the image generation process combined with the camera and monitor effects are also reported.

Huntsberger, Terry↗
NASA NTRS1990

FACE computer simulation

A computer simulation of the FACE (Flexible Arm Controls Experiment) was conducted to assess its design for use in the Space Shuttle. The FACE is supposed to be a 14-ft long articulate structure with 4 degrees of freedom, consisting of shoulder pitch and yaw, elbow pitch, and wrist pitch. Kinematics of the FACE was simulated to obtain data on arm operation, function, workspace and interaction. Payload capture ability was modeled. The simulation indicates the capability for detailed kinematic simulation and payload capture ability analysis, and the feasibility of real-time simulation was determined. In addition, the potential for interactive real-time training through integration of the simulation with various interface controllers was revealed. At this stage, the flexibility of the arm was not yet considered.

Sadeh, Willy Z.↗
NASA NTRS1983

Software simulator for multiple computer simulation system

A description is given of the structure and use of a computer program that simulates the operation of a parallel processor simulation system. The program is part of an investigation to determine algorithms that are suitable for simulating continous systems on a parallel processor configuration. The simulator is designed to accurately simulate the problem-solving phase of a simulation study. Care has been taken to ensure the integrity and correctness of data exchanges and to correctly sequence periods of computation and periods of data exchange. It is pointed out that the functions performed during a problem-setup phase or a reset phase are not simulated. In particular, there is no attempt to simulate the downloading process that loads object code into the local, transfer, and mapping memories of processing elements or the memories of the run control processor and the system control processor. The main program of the simulator carries out some problem-setup functions of the system control processor in that it requests the user to enter values for simulation system parameters and problem parameters. The method by which these values are transferred to the other processors, however, is not simulated.

Ogrady, E. P.↗
NASA NTRS2006

Protocols for Handling Messages Between Simulation Computers

Practical Simulator Network (PSimNet) is a set of data-communication protocols designed especially for use in handling messages between computers that are engaging cooperatively in real-time or nearly-real-time training simulations. In a typical application, computers that provide individualized training at widely dispersed locations would communicate, by use of PSimNet, with a central host computer that would provide a common computational- simulation environment and common data. Originally intended for use in supporting interfaces between training computers and computers that simulate the responses of spacecraft scientific payloads, PSimNet could be especially well suited for a variety of other applications -- for example, group automobile-driver training in a classroom. Another potential application might lie in networking of automobile-diagnostic computers at repair facilities to a central computer that would compile the expertise of numerous technicians and engineers and act as an expert consulting technician.

Balcerowski, John P.↗
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 ↗