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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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Crew Health and Performance Data Analysis Using Change Detection Techniques
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Data Processing and Analysis of Performance Measurements From Ingenuity Rotors in the Jet Propulsion Laboratory 25-Ft Space Simulator
The success of Ingenuity completing over 71 flights on Mars has resulted in the possible use of two further optimized Ingenuity-sized helicopters to retrieve samples for the planned Mars Sample Return campaign. Data to validate performance for several rotor speeds, densities, configurations, and collectives will aid in the design process and help in understanding Ingenuity’s current performance limitations. Tests were performed at the Jet Propulsion Laboratory (JPL) in the 25-foot Space Simulator, which include the Engineering Design Model 1 (EDM-1) with and without a cruciform box, and the Transonic Rotor Test (TRT) which is a single rotor setup featuring the same blade geometry as EDM-1 but designed to spin at much higher RPMs. The experimental setup, test matrix, data processing, data quality, and the performance results for EDM1 and TRT campaigns are presented.
Uncertainty analysis and performance evaluation of thermophysical property measurement of liquid Au in microgravity
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Using Hybrid Techniques to Perform Sensitivity Analysis During the Calibration Process.
Abstract not provided.
HPC Usage Behavior Analysis and Performance Estimation with Machine Learning Techniques
Most researchers with little high performance computing (HPC) experience have difficulties productively using the supercomputing resources. To address this issue, we investigated usage behaviors of the world s fastest academic Kraken supercomputer, and built a knowledge-based recommendation system to improve user productivity. Six clustering techniques, along with three cluster validation measures, were implemented to investigate the underlying patterns of usage behaviors. Besides manually defining a category for very large job submissions, six behavior categories were identified, which cleanly separated the data intensive jobs and computational intensive jobs. Then, job statistics of each behavior category were used to develop a knowledge-based recommendation system that can provide users with instructions about choosing appropriate software packages, setting job parameter values, and estimating job queuing time and runtime. Experiments were conducted to evaluate the performance of the proposed recommendation system, which included 127 job submissions by users from different research fields. Great feedback indicated the usefulness of the provided information. The average runtime estimation accuracy of 64.2%, with 28.9% job termination rate, was achieved in the experiments, which almost doubled the average accuracy in the Kraken dataset.
New Methods and Tools to Perform Safety Analysis within RISMC
The Risk Informed Safety Margins Characterization (RISMC) Pathway uses a systematic approach developed to characterize and quantify safety margins of nuclear power plant structures, systems and components. What differentiates the RISMC approach from traditional probabilistic risk assessment (PRA) is the concept of safety margin. In PRA, a safety metric such as core damage frequency (CDF) is generally estimated using static fault-tree and event-tree models. However, it is not possible to estimate how close we are to physical safety limits (say peak clad temperature) for most accident sequences described in the PRA. In the RISMC approach, what we want to understand is not just the frequency of an event like core damage, but how close we are (or not) to this event and how we might increase our safety margin through margin management strategies in a Dynamic PRA (DPRA) fashion. This paper gives an overview of methods that are currently under development at the Idaho National Laboratory (INL) with the scope of advance the current state of the art of dynamic PRA.
Resilient Plant Monitoring System: Design, Analysis, and Performance Evaluation
Resilient monitoring systems are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools, and the performance of the overall system is evaluated using simulations. The measure of resiliency of the resulting system is evaluated using Kullback Leibler divergence, and is shown to be sufficiently high in all scenarios considered.
CACTUS OPEN SOURCE CODE FOR HYDROKINETIC TURBINE DESIGN AND ANALYSIS: MODEL PERFORMANCE EVALUATION AND PUBLIC DISSEMINATION AS OPEN SOURCE DESIGN TOOL.
Abstract not provided.
Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis
In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here we build on J{\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.
A Methodology for Performing Consequence Analysis for Multi-Unit/Spent Fuel Pool Source Terms.
Abstract not provided.
Performing Consequence Analysis for Multi-Unit/Spent Fuel Pool Source Terms.
Abstract not provided.
Performing Consequence Analysis for Multi-Unit/Spent Fuel Pool Source Terms.
Abstract not provided.
Sensitivity Analysis in Performance Assessment: Towards a Joint Approach.
Abstract not provided.
Sandia HPC cluster performance monitoring analysis & visualization.
Abstract not provided.