Results for “computer simulations” — Page 78

NASA NTRS1982

Flight dynamics analysis and simulation of heavy lift airships. Volume 1: Executive summary

A generic, yet comprehensive mathematical model and computer simulation of the HLA flight dynamics over its entire flight envelope was developed. Implicit in this simulation development are the data reviews and analyses which support the equations of motion and the calculation of forces and moments acting on the vehicle. The simulation, HYBRDS, is addressed to the broad requirements and is intended for use as a synthesis and analysis tool for the evaluation of competing HLA design concepts.

Ringland, R. F.↗

NASA NTRS1984

Improving the Spacelab mass memory unit tape layout with a simulation model

A tape drive called the Mass Memory Unit (MMU) stores software used by Spacelab computers. MMU tape motion must be minimized during typical flight operations to avoid a loss of scientific data. A projection of the tape motion is needed for evaluation of candidate tape layouts. A computer simulation of the scheduled and unscheduled MMU tape accesses is developed for this purpose. This simulation permits evaluations of candidate tape layouts by tracking and summarizing tape movements. The factors that affect tape travel are investigated and a heuristic is developed to find a good tape layout. An improved tape layout for Spacelab I is selected after the evaluation of fourteen candidates. The simulation model will provide the ability to determine MMU layouts that substantially decrease the tape travel on future Spacelab flights.

Noneman, S. R.↗

NASA NTRS1993

Simulations of satellite Doppler wind observations

This study will involve two objectives: (1) to develop, through computer simulations, optimal satellite-based sensor scanning techniques for direct measurement of tropospheric winds on the meso- and synoptic scales; and (2) to construct simulations of remotely measured wind fields for assessing impact of such fields on the diagnosis and prognosis of atmospheric phenomena through the use of Observing System Simulation Experiments (OSSE). Using the LAWS Simulation Model (LSM), various global coverage scenarios have been investigated as part of an effort to define the optimal orbit, configuration and sampling strategies for observations of winds for use in global circulation models. Simulated data sets have been provided to GSFC, FSU and several LAWS team members. Particular emphasis has been on providing realistic cloud cover, cirrus backscatter, aerosol distribution and wind variance on scales less than 600 km. Progress is currently being made to incorporate other remote sensors (AIRS/AMSU, STIKSCAT) into the global OSSEs.

Emmitt, George D.↗

DOE OSTI2018

Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages

This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1985

Modeling of convection phenomena in Bridgman-Stockbarger crystal growth

Thermal convection phenomena in a vertically oriented Bridgman-Stockbarger apparatus were modeled by computer simulations for different gravity conditions, ranging from earth conditions to extremely low gravity, approximate space conditions. The modeling results were obtained by the application of a state-of-the art, transient, multi-dimensional, completely densimetrically coupled, discrete-element computational model which was specifically developed for the simulation of flow, temperature, and species concentration conditions in two-phase (solid-liquid) systems. The computational model was applied to the simulation of the flow and the thermal conditions associated with the convection phenomena in a modified Germanium-Silicon charge enclosed in a stationary fused-silica ampoule. The results clearly indicated that the gravitational field strength influences the characteristics of the coherent vortical flow patterns, interface shape and position, maximum melt velocity, and interfacial normal temperature gradient.

Carlson, F. M.↗

NASA NTRS2006

Software for Simulating Air Traffic

Future Air Traffic Management Concepts Evaluation Tool (FACET) is a system of software for performing computational simulations for evaluating advanced concepts of advanced air-traffic management. FACET includes a program that generates a graphical user interface plus programs and databases that implement computational models of weather, airspace, airports, navigation aids, aircraft performance, and aircraft trajectories. Examples of concepts studied by use of FACET include aircraft self-separation for free flight; prediction of air-traffic-controller workload; decision support for direct routing; integration of spacecraft-launch operations into the U.S. national airspace system; and traffic- flow-management using rerouting, metering, and ground delays. Aircraft can be modeled as flying along either flight-plan routes or great-circle routes as they climb, cruise, and descend according to their individual performance models. The FACET software is modular and is written in the Java and C programming languages. The architecture of FACET strikes a balance between flexibility and fidelity; as a consequence, FACET can be used to model systemwide airspace operations over the contiguous U.S., involving as many as 10,000 aircraft, all on a single desktop or laptop computer running any of a variety of operating systems. Two notable applications of FACET include: (1) reroute conformance monitoring algorithms that have been implemented in one of the Federal Aviation Administration s nationally deployed, real-time, operational systems; and (2) the licensing and integration of FACET with the commercially available Flight Explorer, which is an Internet- based, real-time flight-tracking system.

Sridhar, Banavar↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Predicting Chemical Reaction Barriers with a Machine Learning Model

In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. Here in our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1981

A method of testing attitude control systems during the development phase

A technique, utilized on the Space Telescope Program, and used for testing satellite attitude pointing and control systems during the engineering and development phases is presented. The technique verifies the hardware models used in design phase computer simulations, verifies the interface between the flight hardware and flight software, and uncovers hardware/software switching or mode logic problems. The testing is accomplished in two phases: a dynamic hardware simulator phase using hardware electronic simulators and an electronic vehicle motion simulator; and a second real hardware phase utilizing engineering model gyros and reaction wheels on an airbearing table. Both phases use an engineering model of the flight computer, flight algorithms and software, and a breadboard data management and computer hardware interface for timing simulations. The purpose of each test and the test phases are described, and examples of closed loop test results for both attitude hold and maneuvering models are given.

Besonis, A.↗

NASA NTRS1996

Simulation of Fatigue Behavior of High Temperature Metal Matrix Composites

A generalized relatively new approach is described for the computational simulation of fatigue behavior of high temperature metal matrix composites (HT-MMCs). This theory is embedded in a specialty-purpose computer code. The effectiveness of the computer code to predict the fatigue behavior of HT-MMCs is demonstrated by applying it to a silicon-fiber/titanium-matrix HT-MMC. Comparative results are shown for mechanical fatigue, thermal fatigue, thermomechanical (in-phase and out-of-phase) fatigue, as well as the effects of oxidizing environments on fatigue life. These results show that the new approach reproduces available experimental data remarkably well.

Tong, Mike T.↗

DOE OSTI2024

Performance and Emissions of an SI Engine Fueled With DME-Propane Blends

Dimethyl Ether (DME) is an alternative fuel that can be produced renewably and has the potential for lower CO and NOx emissions than conventional petroleum-based fuels. Blending DME with another gaseous fuel such as propane, which has a lower knock tendency than gasoline, can allow this fuel to be leveraged on SI engines. In this study, the use of DME-propane blends on a spark ignition (SI) engine was studied via computer simulations in order to understand the impact on engine performance and emissions and to identify the knock limitations of using such fuel blends. A 2L Hyundai SI engine was modeled in GT Power and the model was validated by comparing it with computational fluid dynamics (CFD) simulation results. Starting from pure propane, DME was added incrementally until knock was observed in the engine. Results indicate that it is feasible to run propane with DME percentages up to 35% before severe knock impacts were observed. The BTE was higher, but the BSFC also increased for DME-propane blends as compared to gasoline. Here, an increase in NOx emissions was detected along with a significant decrease in CO emissions. CO 2 emissions declined for propane as compared to gasoline but increased with the addition of DME.

alternative fuels↗

NASA NTRS2009

Findings from the Supersonic Qualification Program of the Mars Science Laboratory Parachute System

In 2012, the Mars Science Laboratory Mission (MSL) will deploy NASA's largest extra-terrestrial parachute, a technology integral to the safe landing of its advanced robotic explorer on the surface. The supersonic parachute system is a mortar deployed 21.5 m disk-gap-band (DGB) parachute, identical in geometric scaling to the Viking era DGB parachutes of the 1970's. The MSL parachute deployment conditions are Mach 2.3 at a dynamic pressure of 750 Pa. The Viking Balloon Launched Decelerator Test (BLDT) successfully demonstrated a maximum of 700 Pa at Mach 2.2 for a 16.1 m DGB parachute in its AV4 flight. All previous Mars deployments have derived their supersonic qualification from the Viking BLDT test series, preventing the need for full scale high altitude supersonic testing. The qualification programs for Mars Pathfinder, Mars Exploration Rover, and Phoenix Scout Missions were all limited to subsonic structural qualification, with supersonic performance and survivability bounded by the BLDT qualification. The MSL parachute, at the edge of the supersonic heritage deployment space and 33% larger than the Viking parachute, accepts a certain degree of risk without addressing the supersonic environment in which it will deploy. In addition, MSL will spend up to 10 seconds above Mach 1.5, an aerodynamic regime that is associated with a known parachute instability characterized by significant canopy projected area fluctuation and dynamic drag variation. This aerodynamic instability, referred to as "area oscillations" by the parachute community has drag performance, inflation stability, and structural implications, introducing risk to mission success if not quantified for the MSL parachute system. To minimize this risk and as an alternative to a prohibitively expensive high altitude test program, a multi-phase qualification program using computation simulation validated by subscale test was developed and implemented for MSL. The first phase consisted of 2% of fullscale supersonic wind tunnel testing of a rigid DGB parachute with entry-vehicle to validate two high fidelity computational fluid dynamics (CFD) tools. The computer codes utilized Large Eddy Simulation and Detached Eddy Simulation numerical approaches to accurately capture the turbulent wake of the entry vehicle and its coupling to the parachute bow-shock. The second phase was the development of fluid structure interaction (FSI) computational tools to predict parachute response to the supersonic flow field. The FSI development included the integration of the CFD from the first phase with a finite element structural model of the parachute membrane and cable elements. In this phase, a 4% of full-scale supersonic flexible parachute test program was conducted to provide validation data to the FSI code and an empirical dataset of the MSL parachute in a flight-like environment. The final phase is FSI simulations of the full-scale MSL parachute in a Mars type deployment. Findings from this program will be presented in terms of code development and validation, empirical findings from the supersonic testing, and drag performance during supersonic operation.

EDL↗

NASA NTRS1994

Modeling of stress/strain behavior of fiber-reinforced ceramic matrix composites including stress redistribution

A computational simulation procedure is presented for nonlinear analyses which incorporates microstress redistribution due to progressive fracture in ceramic matrix composites. This procedure facilitates an accurate simulation of the stress-strain behavior of ceramic matrix composites up to failure. The nonlinearity in the material behavior is accounted for at the constituent (fiber/matrix/interphase) level. This computational procedure is a part of recent upgrades to CEMCAN (Ceramic Matrix Composite Analyzer) computer code. The fiber substructuring technique in CEMCAN is used to monitor the damage initiation and progression as the load increases. The room-temperature tensile stress-strain curves for SiC fiber reinforced reaction-bonded silicon nitride (RBSN) matrix unidirectional and angle-ply laminates are simulated and compared with experimentally observed stress-strain behavior. Comparison between the predicted stress/strain behavior and experimental stress/strain curves is good. Collectively the results demonstrate that CEMCAN computer code provides the user with an effective computational tool to simulate the behavior of ceramic matrix composites.

Mital, Subodh K.↗

DOE OSTI2016

Power grid simulation applications developed using the GridPACK™ high performance computing framework

Not Available

Jin, Shuangshuang↗

DOE OSTI2025

Model Form Error Correction for a Black-Box Thermal Battery Heat Transfer Simulation

Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.

Batteries↗

NASA NTRS1974

Simulation of pitch angle diffusion of charged particles in a disordered magnetic field

Results are reported for computer simulation experiments in which a statistical ensemble of random magnetic field realizations is generated, orbits of charged particles in the random fields are followed, and a pitch-angle diffusion coefficient is derived from the temporal evolution of the orbits. Diffusion coefficients predicted by three nonlinear theories are compared with the derived coefficients for the standard quasilinear theory of velocity diffusion, and the goals of future simulations are outlined.

Kaiser, T. B.↗

NASA NTRS1975

Performance evaluation using SYSTID time domain simulation

This paper reviews the SYSTID technique for performance evaluation of communication systems using time-domain computer simulation. An example program illustrates the language. The inclusion of both Gaussian and impulse noise models make accurate simulation possible in a wide variety of environments. A very flexible postprocessor makes possible accurate and efficient performance evaluation.

Tranter, W. H.↗