Aerodynamic characterization of a Delta-wing UAV based on real flight data processing

2021 ◽  
Author(s):  
P. Longobardi ◽  
G. Laupre ◽  
J. Skaloud
Keyword(s):  
Data Processing ◽  
Delta Wing ◽  
Flight Data

scholarly journals Robust measurements of electric conductivity in polyethylene based materials: measurement setup, data processing and impact of sample preparation

2017 ◽  
Author(s):  
Xiangdong Xu ◽  
M Karlsson ◽  
K. Gaska ◽  
Stanislaw Gubanski ◽  
Henrik Hillborg ◽  
...  
Keyword(s):  
Data Processing ◽  
Electric Conductivity ◽  
External Noise ◽  
Current Data ◽  
Current Level ◽  
Conductivity Measurements ◽  
High Reproducibility ◽  
Robust Measurements ◽  
Good Differentiation

<p>In this work, a measurement setup and an algorithm are introduced that allow for obtaining and processing the current data during standard measurements of electric conductivity in polymeric insulation materials. Robustness of this setup is demonstrated through dc conductivity measurements of low density polyethylene (LDPE) specimens that were prepared by two different techniques, i.e. compression molding and extrusion, at two independent laboratories (at Chalmers and KTH) from the same raw pellets. The results resented in this paper reveal that the implemented data processing algorithm allows for clear separation of the conduction current level and the external noise and thus for good differentiation between the conductivity levels in the investigated material specimens. In addition, a high reproducibility of the results is obtained for the independently manufactured specimens, suggesting the proposed measurement technique is well suitable for characterization of low conducting dielectrics.</p>

BeerMIMS: Exploring the Use of Membrane-Inlet Mass Spectrometry (MIMS) Coupled to KNIME for the Characterization of Danish Beers

2022 ◽  
pp. 146906672110733
Author(s):  
Sean Sebastian Hughes ◽  
Marcus M. K. Hughes ◽  
Rasmus Voersaa Jonsbo ◽  
Carsten Uhd Nielsen ◽  
Frants Roager Lauritsen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Complex Nature ◽  
Feature Identification ◽  
Computing Power ◽  
Membrane Inlet Mass Spectrometry ◽  
Data Processing Software ◽  
Open Source Data ◽  
Free Open Source

Beer is a complex mix of more than 7700 compounds, around 800 of which are volatile. While GC-MS has been actively employed in the analysis of the volatome of beer, this method is challenged by the complex nature of the sample. Herein, we explored the possible of using membrane-inlet mass spectrometry (MIMS) coupled to KNIME to characterize local Danish beers. KNIME stands for Konstanz Information Miner and is a free open-source data processing software which comes with several prebuilt nodes, that, when organized, result in data processing workflows allowing swift analysis of data with outputs that can be visualized in the desired format. KNIME has been shown to be promising in automation of large datasets and requires very little computing power. In fact, most of the computations can be carried out on a regular PC. Herein, we have utilized a KNIME workflow for data visualization of MIMS data to understand the global volatome of beers. Feature identification was not possible as of now but with a combination of MIMS and a KNIME workflow, we were able to distinguish beers from different micro-breweries located in Denmark, laying the foundation for the use of MIMS in future analysis of the beer volatome.

Data Processing Modeling for Reference Materials Preparation

2013 ◽  
Vol 846-847 ◽  
pp. 1334-1338
Author(s):  
Ting Zhang ◽  
Jia Li Gu
Keyword(s):  
Data Processing ◽  
Reference Materials ◽  
Uncertainty Estimation ◽  
Materials Preparation

This paper focuses on the data processing modeling for reference materials preparation. By analyzing the data of reference materials (RMs), the process of repeatability and stability verification, uncertainty estimation, traceability of the value of a quantity and the characterization of RMs are detailed in the paper.

Proposed Changes to the ASME Boiler and Pressure Vessel Code Section III Appendix N for Flow-Induced Vibrations

2015 ◽  
Author(s):  
Gregory A. Banyay ◽  
Gregory A. Meyer ◽  
Adam P. Walker
Keyword(s):  
Data Processing ◽  
Pressure Vessel ◽  
Structural Damping ◽  
Structural Dynamic ◽  
Acoustic Damping ◽  
Flow Induced Vibrations ◽  
Processing Algorithms ◽  
Complex Phenomena ◽  
Proper Orthogonal

This article outlines changes proposed for implementation in the ASME Boiler and Pressure Vessel Code Section III Appendix N pertaining to Flow-Induced Vibrations (FIV). Several portions of Appendix N were originally written multiple decades ago and are not necessarily readily prescriptive for present-day analyses. An ASME Task Group on Flow-Induced Vibrations has been formed to identify areas of improvement to Appendix N so that the Appendix may be more useable in present-day FIV analyses, and a means of establishing consensus for characterization of this complex phenomena. This paper identifies opportunities for improvement to portions of Appendix N pertaining to Turbulence, Acoustic, and associated Structural Dynamic Modeling, and Data Processing. Specific suggestions are made for the following: Characterization of acoustic damping and acoustic harmonic responses, updated considerations for aeroacoustic phenomena, structural damping quantification of nuclear components, systematic approaches to modeling (random) turbulence-induced vibrations (including RMS-to-Peak ratio), considerations for leakage flow-induced instabilities, and opportunities to employ computational methods are discussed. Opportunities for the applicability of data processing algorithms such as the proper orthogonal decomposition and circumferential wavenumber decomposition are also discussed and an updated methodology for combination of random and deterministic loads for FIV analyses is presented.

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