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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The ATLAS Trigger Simulation with Legacy Software

Physics analyses at the LHC require accurate simulations of the detector response and the event selection processes, generally done with the most recent software releases. The trigger response simulation is crucial for determination of overall selection efficiencies and signal sensitivities and should be done with the same software release with which data were recorded. This requires potentially running with software dating many years back, the so-called legacy software, in which algorithms and configuration may differ from their current implementation. Therefore having a strategy for running legacy software in a modern environment becomes essential when data simulated for past years start to present a sizeable fraction of the total. The requirements and possibilities for such a simulation scheme within the ATLAS software framework were examined and a proof-of-concept simulation chain has been successfully implemented. One of the greatest challenges was the choice of a data format which promises long term compatibility with old and new software releases. Over the time periods envisaged, data format incompatibilities are also likely to emerge in databases and other external support services. Software availability may become an issue, when e.g. the support for the underlying operating system might stop. The encountered problems and developed solutions will be presented, and proposals for future development will be discussed. Some ideas reach beyond the retrospective trigger simulation scheme in ATLAS as they also touch more generally aspects of data preservation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

Engineering↗
NASA NTRS1979

A real-time simulation facility for advanced digital guidance and control system research

A real-time simulation facility built at NASA's Langley Research Center to support digital guidance and control research and development activities is examined. The unit has recently been used to develop autoland systems for VTOL. The paper describes the autoland experiment and the flight environment, the simulation facility hardware and software, and presents typical simulation data to illustrate the type of data analysis carried out during software development. Finally, flight data for a later version of the autoland system are presented to demonstrate the simulation's capability to predict overall system behavior.

Bryant, W. H.↗
NASA NTRS1983

LANDSAT-4 image data quality analysis for energy related applications

No useable LANDSAT 4 TM data were obtained for the Hanford site in the Columbia Plateau region, but TM simulator data for a Virginia Electric Company nuclear power plant was used to test image processing algorithms. Principal component analyses of this data set clearly indicated that thermal plumes in surface waters used for reactor cooling would be discrenible. Image processing and analysis programs were successfully testing using the 7 band Arkansas test scene and preliminary analysis of TM data for the Savanah River Plant shows that current interactive, image enhancement, analysis and integration techniques can be effectively used for LANDSAT 4 data. Thermal band data appear adequate for gross estimates of thermal changes occurring near operating nuclear facilities especially in surface water bodies being used for reactor cooling purposes. Additional image processing software was written and tested which provides for more rapid and effective analysis of the 7 band TM data.

Wukelic, G. E.↗
NASA NTRS1986

Comparison of modal identification techniques using a hybrid-data approach

Modal identification of seemingly simple structures, such as the generic truss is often surprisingly difficult in practice due to high modal density, nonlinearities, and other nonideal factors. Under these circumstances, different data analysis techniques can generate substantially different results. The initial application of a new hybrid-data method for studying the performance characteristics of various identification techniques with such data is summarized. This approach offers new pieces of information for the system identification researcher. First, it allows actual experimental data to be used in the studies, while maintaining the traditional advantage of using simulated data. That is, the identification technique under study is forced to cope with the complexities of real data, yet the performance can be measured unquestionably for the artificial modes because their true parameters are known. Secondly, the accuracy achieved for the true structural modes in the data can be estimated from the accuracy achieved for the artificial modes if the results show similar characteristics. This similarity occurred in the study, for example, for a weak structural mode near 56 Hz. It may even be possible--eventually--to use the error information from the artificial modes to improve the identification accuracy for the structural modes.

Pappa, Richard S.↗
NASA NTRS2007

Measurement and Simulation of the Variation in Proton-Induced Energy Deposition in Large Silicon Diode Arrays

The proton induced charge deposition in a well characterized silicon P-i-N focal plane array is analyzed with Monte Carlo based simulations. These simulations include all physical processes, together with pile up, to accurately describe the experimental data. Simulation results reveal important high energy events not easily detected through experiment due to low statistics. The effects of each physical mechanism on the device response is shown for a single proton energy as well as a full proton space flux.

Howe, Christina L.↗
NASA NTRS2010

Strain System for the Motion Base Shuttle Mission Simulator

The Motion Base Shuttle Mission Simulator (MBSMS) Strain System is an innovative engineering tool used to monitor the stresses applied to the MBSMS motion platform tilt pivot frames during motion simulations in real time. The Strain System comprises hardware and software produced by several different companies. The system utilizes a series of strain gages, accelerometers, orientation sensor, rotational meter, scanners, computer, and software packages working in unison. By monitoring and recording the inputs applied to the simulator, data can be analyzed if weld cracks or other problems are found during routine simulator inspections. This will help engineers diagnose problems as well as aid in repair solutions for both current as well as potential problems.

Huber, David C.↗
NASA NTRS2011

Development of the Sasquatch Drop Test Footprint Tool

The Crew Exploration Vehicle Parachute Assembly System (CPAS) is the parachute system for NASA s Orion spacecraft. CPAS is currently in the design and testing phase of development. The test program consists of numerous drop tests, wherein a test article rigged with parachutes is extracted or released from an aircraft. During such tests, range safety is paramount, as is the recoverability of the parachutes and test article. It is crucial to establish an aircraft release point that will ensure that the article and all items released from it will land in safe locations. Early in the CPAS project, a legacy tool (previously used on the X-38 project) was used to determine a safe release point and to predict the landing locations (the footprint) of the payload and all released objects. Due to increasing test complexity and the need for a more flexible tool, a new footprint predictor tool, called Sasquatch, was created in MATLAB. This tool takes in a simulated trajectory for the test article, information about all released objects, and atmospheric wind data (simulated or actual) to calculate the trajectories of the released objects. Dispersions are applied to the landing locations of those objects, taking into account the variability of winds, aircraft release point, and object descent rate. A safe aircraft release point is determined based on the landing locations of the payload and released objects. The release point, landing locations, and dispersions are plotted on a simple map of the drop zone for easy reference. To date, Sasquatch has been used for thirteen drop tests. Comparing the predictions with actual test results has allowed for significant improvements in the tool s predictive capabilities, especially the incorporation of a well-correlated horizontal throw model. Intended future improvements to the tool include tighter dispersions on the landing locations, Monte Carlo capability, direct input from trajectory simulations, and a graphical user interface.

Bledsoe, Kristin J.↗
DOE OSTI2017

Simulating, visualizing, and searching traffic in a software defined network

The present disclosure pertains to systems and methods for simulating data packet routing within a software defined network (“SDN”), visualizing the results of the simulation, and permitting a user to search the resulting simulation. In one specified embodiment, a system may receive from a user a simulation parameter associated with a packet to be simulated in the SDN. A packet based on the at least one simulation parameter may be generated. A response of the SDN to the packet may be simulated by identifying applicable traffic routing rules and identifying a subsequent destination based on the applicable traffic routing rules. A record of the subsequent destination may be added to the simulation result, and the process may continue until a terminating condition is satisfied.

42 ENGINEERING↗
DOE OSTI2016

MARMOT Modeling of Microstructure-Dependent Thermal Conductivity in Nuclear Fuels

Uranium dioxide (UO{sub 2}) is the primary nuclear fuel in light water reactors. The thermal conductivity of UO{sub 2} is an important fuel performance metric as it affects nuclear energy conversion efficiency and safety. Unfortunately UO{sub 2} has a very low thermal conductivity, even in the single crystal form. In reactor environment, the radiation-induced defects, fission products, and complex microstructural evolution degrade the thermal conductivity of UO{sub 2} significantly. These defects and microstructures in UO{sub 2} fuels include point defects, gas bubbles, metallic precipitates, etc.. In the rim region of a fuel pellet, high burnup structure also forms which contains many nanograins and thus high density of grain boundaries. Therefore, understanding how different irradiation-induced microstructures affect the thermal conductivity in nuclear fuels is important for predicting the overall thermal transport properties in nuclear fuels. In this work, the unit effect of each microstructure on the thermal conductivity of UO{sub 2} is evaluated, using grain boundary as an example. The obtained unit mechanism can be used as input for the multiplier model that describes the combined effect of various microstructures. In this work we use phase field simulations coupled with heat conduction calculations to study the co-evolution of grain size and thermal conductivity. The simulation data are used to calibrate an analytical model. Based on the sufficient statistics from simulations, we conclude that the analytical model can well predict the grain-size-dependent thermal conductivity over a wide range of grain sizes if the input parameters are calibrated. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2022

Characterizing and Mitigating Intraday Variability: Reconstructing Source Structure in Accreting Black Holes with mm-VLBI

The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Streaming Data in HPC Workflows Using ADIOS

The “IO Wall” problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between workflow stages. One way to avoid this problem in scientific workflows is to stream data directly from producers to consumers and avoiding storage entirely. However, the manner in which this is accomplished is key to both performance and usability. This paper presents the Sustainable Staging Transport, an approach which allows direct streaming between traditional file writers and readers with few application changes. SST is an ADIOS “engine”, accessible via standard ADIOS APIs, and because ADIOS allows engines to be chosen at run-time, many existing file-oriented ADIOS workflows can utilize SST for direct application-to-application communication without any source code changes. This paper describes the design of SST and presents performance results from various applications that use SST, for feeding model training with simulation data with substantially higher bandwidth than the theoretical limits of Frontier’s file system, for strong coupling of separately developed applications for multiphysics multiscale simulation, or for in situ analysis and visualization of data to complete all data processing shortly after the simulation finishes.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗
DOE OSTI2019

Constraining the neutron-matter equation of state with gravitational waves

We demonstrate how observations of gravitational waves from binary neutron star (BNS) mergers over the next few years can be combined with insights from nuclear physics to obtain useful constraints on the equation of state (EoS) of dense matter. In particular, the neutron-matter EoS between 1 and 2 times the nuclear saturation density $n_0$ ≈ 0.16 fm –3 can be constrained to within 20%, given the simulated data from about 15 merger events. Using Fisher information methods, we combine observational constraints from simulated BNS merger events drawn from various population models with independent measurements of the neutron star radii expected from x-ray astronomy [the Neutron Star Interior Composition Explorer observations in particular] to directly constrain nuclear physics parameters. To parametrize the nuclear EoS, we use a different approach, expanding from pure nuclear matter rather than from symmetric nuclear matter to make use of recent quantum Monte Carlo calculations. This method eschews the need to invoke the so-called parabolic approximation to extrapolate from symmetric nuclear matter, allowing us to directly constrain the neutron-matter EoS. Using a principal component analysis, we identify the combination of parameters most tightly constrained by observational data. We discuss sensitivity to various effects such as different component masses through population-model sensitivity, phase transitions in the core EoS, and large deviations from the central parameter values.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

Tool for Rapid Analysis of Monte Carlo Simulations

Designing a spacecraft, or any other complex engineering system, requires extensive simulation and analysis work. Oftentimes, the large amounts of simulation data generated are very di cult and time consuming to analyze, with the added risk of overlooking potentially critical problems in the design. The authors have developed a generic data analysis tool that can quickly sort through large data sets and point an analyst to the areas in the data set that cause specific types of failures. The Tool for Rapid Analysis of Monte Carlo simulations (TRAM) has been used in recent design and analysis work for the Orion vehicle, greatly decreasing the time it takes to evaluate performance requirements. A previous version of this tool was developed to automatically identify driving design variables in Monte Carlo data sets. This paper describes a new, parallel version, of TRAM implemented on a graphical processing unit, and presents analysis results for NASA's Orion Monte Carlo data to demonstrate its capabilities.

Restrepo, Carolina↗
DOE OSTI2013

Simulation and big data challenges in tuning building energy models

EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to “get it right”. This process can sometimes take months. “Autotune” is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this paper, we describe the scope of the problem, particularly the technical challenges faced and overcome, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes. We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

Sanyal, Jibonananda↗
NASA NTRS1991

Interactive atmospheric data access via high-speed networks

The VIS-5D system running on large workstations lets scientists interactively explore atmospheric simulation data sets containing up to 5 x 10(exp 7) points. A distributed version of VIS-5D running on a workstation and a supercomputer will make it possible to interactively explore data sets containing up to 10(exp 10) points. Wide area gigabit networks will bring this capability to scientists at most academic and research institutions. This software will help scientists to interactively develop numerical models of atmospheric phenomena.

Hibbard, William↗