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Results for “error models”

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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Lattice Element Error Solver for Modeling of Accelerators, Storage Ring, Transport Systems, and Insertion Devices

The goals of CRADA 350 were achieved in this Phase I SBIR work. Specifically, a software environment was developed which included a nonlinear solver, OASIS. During the Phase I work, we applied the algorithm to a number of nonlinear-solver benchmark problems, including SLAC accelerator problems. By searching over only two optimization-control parameters, this Phase I work has enabled the development of a systematic procedure to facilitate the finding of a “best-guessed” global minimum solution for various types of non-linear problems with different size and complexity. This set of rules was codified and led to a versatile nonlinear program, IMIGO.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

High-Resolution Regional Wave Hindcast for the U.S. Alaska Coast

Regional long-term wave hindcast with state-of-art third-generation models is essential for characterization of regional wave resources. This report summarizes modeling efforts for the simulation of the wave climate along southern Alaska, which used an unstructured, nested-grid modeling approach that incorporates a global-regional nested grid using WAVEWATCH III ® and the high-resolution SWAN (Simulating Waves Nearshore) model. Wave resource and wave bulk parameters were simulated for a 32-year period from 1979 to 2010 and were subsequently validated with wave buoy data in the model domain. Error statistics of model skills were calculated at all buoy stations. Overall, model results, both resource and bulk parameters, match observations well. The model hindcast was able to reproduce the seasonal variation of the sea state with large waves that occur in the winter and early spring months when wind forcing is strong and the calm sea state during the summer when wind forcing is weak. The hindcast also considers the effect of sea ice on wave growth, which affects the southern Bering Sea. The hindcast results, including resource and bulk parameters, were archived at every model grid point for the entire model domain at three-hour intervals. In addition, spectral results were saved at National Data Buoy Center buoy stations and selected virtual buoy locations at hourly intervals. The nested-grid modeling framework employed in this study provides a powerful and efficient modeling approach for accurately simulating wave climate at regional and long-term temporal scales with sufficiently fine resolutions in the nearshore region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Robust Bayesian Calibration of a k-ε Model for Compressible Jet-in-Crossflow Simulations

Compressible jet-in-crossflow interactions are difficult to simulate accurately using Reynolds-averaged Navier–Stokes (RANS) models. This could be due to simplifications inherent in RANS or the use of inappropriate RANS constants estimated by fitting to experiments of simple or canonical flows. Our previous work on Bayesian calibration of a k-ε model to experimental data had led to a weak hypothesis that inaccurate simulations could be due to inappropriate constants more than model-form inadequacies of RANS. In this work, Bayesian calibration of k-ε constants to a set of experiments that span a range of Mach numbers and jet strengths has been performed. The variation of the calibrated constants has been checked to assess the degree to which parametric estimates compensate for RANS’s model-form errors. An analytical model of jet-in-crossflow interactions has also been developed, and estimates of k-ε constants that are free of any conflation of parametric and RANS’s model-form uncertainties have been obtained. It has been found that the analytical k-ε constants provide mean-flow predictions that are similar to those provided by the calibrated constants. Further, both of them provide predictions that are far closer to experimental measurements than those computed using “nominal” values of these constants simply obtained from the literature. It can be concluded that the lack of predictive skill of RANS jet-in-crossflow simulations is mostly due to parametric inadequacies, and our analytical estimates may provide a simple way of obtaining predictive compressible jet-in-crossflow simulations.

42 ENGINEERING↗
NASA NTRS1986

Error-source effects in a high-accuracy optical finite-element processor

High-accuracy optical linear algebra processors are addressed with attention to three new aspects. These include: their application to the solution of finite-element problems; the first error-source models for component errors in such processors; and the first analysis of error sources in such processors.

Taylor, B. K.↗
NASA NTRSDate not supplied

Can Resilience Assessments Inform Early Design Human Factors Decision-making?

There is a growing call among researchers for tighter coupling between human factors and human reliability assessments. In this research, we explore if early design stage resilience assessments can help bridge some of the gaps between human factors and human reliability assessments. Resilience in systems is their ability to recover reasonably and operate within acceptable bounds during failures and unexpected events. The fmdtools toolkit allows designers to assess the resilience of a system by modeling the human error and machine-related failure propagation in both nominal and faulty scenarios during the early design stages. As a result, the fmdtools toolkit has a low-fidelity dynamic human reliability assessment component built into it. In this paper, we study if the results from the fmdtools simulations can help inform and prioritize human factors design decision-making, resulting in tighter coupling between human factors and human reliability assessments during the design process. Specifically, we explore the results from a rover design example to understand the types of information that can help guide human factor-related decision-making.

Resilience-based Design↗
NASA NTRS2012

Predicting Pilot Error in Nextgen: Pilot Performance Modeling and Validation Efforts

We review 25 articles presenting 5 general classes of computational models to predict pilot error. This more targeted review is placed within the context of the broader review of computational models of pilot cognition and performance, including such aspects as models of situation awareness or pilot-automation interaction. Particular emphasis is placed on the degree of validation of such models against empirical pilot data, and the relevance of the modeling and validation efforts to Next Gen technology and procedures.

pilot error↗