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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Enhancing the CDF's B physics program with a faster data acquisition system.

The physics program of Run II at the Tevatron includes precision electroweak measurements such as the determination of the top quark and W boson masses; bottom and charm physics including the determination of the B{sub s} and D{sup 0} mixing parameters; studies of the strong interaction; and searches for the Higgs particle, supersymmetric particles, hidden space-time dimensions and quark substructure. All of these measurements benefit from a high-resolution tracking detector. Most of them rely heavily on the efficient identification of heavy flavored B hadrons by detection of displaced secondary vertices, and are enhanced by the capability to trigger on tracks not coming from the primary vertex. This is uniquely provided by CDF's finely-segmented silicon detectors surrounding the interaction region. Thus CDF experiment's physics potential critically depends on the performance of its silicon detectors. The CDF silicon detectors were designed to operate up to 2-3 fb{sup -1} of accumulated pji collisions, with an upgrade planned thereafter. However, the upgrade project was canceled in 2003 and Run II has been extended through 2011, with an expected total delivered integrated luminosity of 12 fb{sup -1} or more. Several preventive measures were taken to keep the original detector operational and maintain its performance. The most important of these are the decrease in the operating temperature of the detector, which reduces the impact of radiation exposure, and measures to minimize damage due to integrated radiation dose, thermal cycles, and wire bond resonance conditions. Despite these measures the detectors operating conditions continue to change with issues arising from radiation damage to the sensors, aging infrastructure and electronics. These, together with the basic challenges posed by the inaccessibility of the detector volume and large number (about 750 thousand) of readout channels, make the silicon detector operations the single most complex and high priority job in the CDF experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Analysis of Femtosecond Timing Noise and Stability in Microwave Components

To probe chemical dynamics, X-ray pump-probe experiments trigger a change in a sample with an optical laser pulse, followed by an X-ray probe. At the Linac Coherent Light Source, LCLS, timing differences between the optical pulse and x-ray probe have been observed with an accuracy as low as 50 femtoseconds. This sets a lower bound on the number of frames one can arrange over a time scale to recreate a 'movie' of the chemical reaction. The timing system is based on phase measurements from signals corresponding to the two laser pulses; these measurements are done by using a double-balanced mixer for detection. To increase the accuracy of the system, this paper studies parameters affecting phase detection systems based on mixers, such as signal input power, noise levels, temperature drift, and the effect these parameters have on components such as the mixers, splitters, amplifiers, and phase shifters. Noise data taken with a spectrum analyzer show that splitters based on ferrite cores perform with less noise than strip-line splitters. The data also shows that noise in specific mixers does not correspond with the changes in sensitivity per input power level. Temperature drift is seen to exist on a scale between 1 and 27 fs/{sup o}C for all of the components tested. Results show that any components using more metallic conductor tend to exhibit more noise as well as more temperature drift. The scale of these effects is large enough that specific care should be given when choosing components and designing the housing of high precision microwave mixing systems for use in detection systems such as the LCLS. With these improvements, the timing accuracy can be improved to lower than currently possible.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Criticality Calculations with MCNP6 - Practical Lectures

These slides are used to teach MCNP (Monte Carlo N-Particle) usage to nuclear criticality safety analysts. The following are the lecture topics: course information, introduction, MCNP basics, criticality calculations, advanced geometry, tallies, adjoint-weighted tallies and sensitivities, physics and nuclear data, parameter studies, NCS validation I, NCS validation II, NCS validation III, case study 1 - solution tanks, case study 2 - fuel vault, case study 3 - B&W core, case study 4 - simple TRIGA, case study 5 - fissile mat. vault, criticality accident alarm systems. After completion of this course, you should be able to: Develop an input model for MCNP; Describe how cross section data impact Monte Carlo and deterministic codes; Describe the importance of validation of computer codes and how it is accomplished; Describe the methodology supporting Monte Carlo codes and deterministic codes; Describe pitfalls of Monte Carlo calculations; Discuss the strengths and weaknesses of Monte Carlo and Discrete Ordinants codes; The diffusion theory model is not strictly valid for treating fissile systems in which neutron absorption, voids, and/or material boundaries are present. In the context of these limitations, identify a fissile system for which a diffusion theory solution would be adequate.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Hardening of DOE UQ Codes for Ultra-Deep Proactive Geosteering in Oilfield Service

The primary objective of this project was to harden the DOE uncertainty quantification (UQ) code to solve the inverse problem for the geosteering service in the oilfield drilling and exploration. The primary focus is to verify the feasibility of using QUESO to solve geosteering inverse problems through statistical inversion. QUESO is a software library developed by QUEST, a DOE's Scientific Discovery through Advanced Computing (SciDAC) institute. In the process of drilling a borehole, geosteering is the act of adjusting the well trajectory on the fly to reach the geological targets or to keep the wellbore within desired formation. Geosteering inversion is to infer underground formation structure using measurements such as azimuthal resistivity logging data. The geosteering process relies on the real-time reconstruction of the subsurface earth model, and a robust and efficient inversion algorithm is always necessary to satisfy the job requirement. The new generation of azimuthal resistivity geosteering tool is capable of detecting multiple formation boundaries 20-100 ft away, and the corresponding geosteering inversion problem can be very challenging as many parameters need to be inverted within such a large depth of investigation. The success of the project will support US energy security by helping to advance subsurface modeling and characterization technologies in oilfield applications. An effective subsurface inversion algorithm and the implementation as a software package will promote the usage of ultra-deep azimuthal resistivity measurements, which can help well placement in complicated underground environments, reduce costs and risks of drilling, and increase oil and gas production. It will help medium and small size independent service companies and tool manufactures to enter the more advanced and lucrative directional drilling area. In addition, by hardening the QUESO code and building a transparent interface for easy manipulation of the QUESO functions, we provide a user-friendly tool for a larger scientific and industry communities that rely heavily on modeling and simulation tools for forecasting, parameter studies, design, and decision making. These are all activities that can strongly benefit from meaningful representations of modeling uncertainty supported by our product.

58 GEOSCIENCES↗
DOE OSTI2018

Dynamic Magnetic Compaction of Porous Materials Final Report CRADA No. TSB-0990-94

The purpose of this CRADA project was to predict and verify optimum pressure vs. time history for the compaction of porous copper and tungsten. LLNL modeled the rapid compaction of powdered material from an initial density of about 30% theoretical maximum to more than 90% theoretical maximum. The compaction simulations were benchmarked against existing data and new data was acquired by IAP Research. The modeling was used to perform parameter studies on the pressure loading time history, initial porosity and temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2018

PAGOSA Simulations for the LANL Blast Tube

A parameter study was performed for the Los Alamos National Laboratory Blast Tube to tune geometry to best replicate peak blast pressures from experiments. Charge placement, reflecting wall thickness, flange gap width, and reflecting wall stand-off were sampled using a Latin-Hypercube design to train a Radial Basis Function Network surrogate model. This surrogate model was then used with a Nelder - Mead Simplex minimization routine to minimize error for the surrogate predictions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Head Impact from Falling Payload of a Small Balloon

Despite the increasing number of small scientific balloon missions with payloads in the gram-to- kilogram mass range, little is known about the injury risk they pose to humans on the ground. We investigated the risk of head injury using the head injury criterion (HIC) from impact with a 1.54 kg (3.40 pound) payload. Study parameters were impact speeds of 670, 1341, and 2012 cm s -1 (15, 30, and 45 mph) and protective padding wall thicknesses between zero and 10 cm (3.9 inch). Padding provided meaningful reductions of injury risk outcomes at all speeds. The maximum risk of AIS 3+ injury was approximately 3.6% (HIC 249) for the 670 cm s -1 (15 mph) case with 0.5 cm (0.2 inch) of padding, 34% (HIC 801) for the 1341 cm s -1 (30 mph) case with 3.0 cm (1.2 inch) of padding, and 67% (HIC 1147) for the 2012 cm s -1 (45 mph) case with 7.0 cm (2.8 inch) of padding. Adding 1.0 cm (0.39 inch) of padding to these two latter cases reduced AIS 3+ injury risk to approximately 13% (HIC 498) and 37% (HIC 835), respectively. Public safety can be increased when balloon operators use padded payload enclosures as adjuncts to parachutes.

42 ENGINEERING↗
DOE OSTI2019

Improved Time-Stepping Methods in Global to Regional Ocean Modeling

Improvements in time-stepping schemes can provide substantial improvements in the throughput of global ocean-climate models, but is challenging due to large variations in time scales in the dynamics and spatial scales in the global, variable resolution mesh. Two funded projects aim to speed up the Model for Prediction Across Scales-Ocean (MPAS-Ocean). The first will add a variable-timestepping formulation to MPAS-Ocean, which is a critical step towards running efficiently on global variable-resolution meshes with regional and coastal refinement. The second optimizes the barotropic (depth-integrated) mode, which is currently a performance bottleneck at high core counts. The proposed compute time will be used for extensive model validation and parameter studies for both methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

M3 Wave System Modeling: Cooperative Research and Development Final Report, CRADA Number CRD-17-697

M3 Wave is a leader in submerged pressure differential wave energy harvesting technology. M3 Wave's approach to developing technology is focused on simplicity and survivability. The company has been working on wave energy devices since 1991 and has produced a number of successes during that time. During testing as part of DOE's Wave Prize finals, M3 Wave encountered an unexpected phenomenon that increased power production by over 25% in their NEXUS system. The problem to be explored by this project is how to accurately model and characterize this phenomenon to better take advantage of the potential benefits. This will be accomplished by: 1) developing and validating a system-hydrodynamics model of M3 Wave's NEXUS system; 2) performing a parameter study to identify possible mechanisms for improved energy capture, and 3) providing M3 Wave with a working, adaptable, model of their system, such that M3 Wave is able to incorporate the study's findings and optimize their system for improved LCOE.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Sensitivity Analysis of Salt Storage Cavern Mechanical Integrity Test Parameters

This report examines the sensitivity of salt cavern Mechanical Integrity Tests (MIT) to uncertainties in key test parameters. MIT's are used by cavern operators to detect and quantify leak rates in access wells in underground salt storage caverns and involve a suite of measured and assumed parameters that have a direct impact on the sensitivity of the testing to detect actual leaks from the cavern storage system. Determining the sensitivity of the testing to these different parameters provides a basis for understanding the results from, and informing the design criteria for, this type of testing. Without fully understanding the sensitivity of the test to the testing parameters, it is possible that the test results may not accurately reflect the integrity of the cavern system; an actual leak may be missed, or an intact system may be interpreted as leaking. This report reviews the main parameters included in MITs and examines how selected changes in their values can impact test results. The deviations used in the sensitivity analyses were designed to be within the ranges believed to be similar to those which may be encountered during testing. The results show that small, plausible fluctuations in some of the parameters measured values can have a significant impact on the testing results. Of the parameters studied here, the sensitivity analyses showed the order of importance to be (from highest to lowest): nitrogen-oil interface depth measurement, well bore temperature, well head pressure, and finally the internal geometry of the testing interval.

42 ENGINEERING↗
DOE OSTI2020

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗