Engine Test Data Quality Requirements for Model Based Calibration: A Testing and Development Efficiency Opportunity

2013 ◽  
Author(s):  
Tim Beattie ◽  
Richard P. Osborne ◽  
Wilhelm Graupner
Keyword(s):  
Data Quality ◽  
Test Data ◽  
Engine Test ◽  
Quality Requirements ◽  
Model Based

scholarly journals Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications

2021 ◽  
Vol 127 ◽  
pp. 103414
Author(s):  
N. Omri ◽  
Z. Al Masry ◽  
N. Mairot ◽  
S. Giampiccolo ◽  
N. Zerhouni
Keyword(s):  
Fault Detection ◽  
Data Quality ◽  
Quality Requirements

scholarly journals KOMPARASI MODEL PEMBELAJARAAN KOOPERATIF TIPE MAKE A MATCH DAN TIPE TEAM GAMES TOURNAMENT (TGT) PADA MATERI KONSEP MOL TERHADAP HASIL BELAJAR SISWA KELAS X SMA KARTIKA XIII-1 AMBON

2019 ◽  
Vol 9 (1) ◽  
pp. 31-37
Author(s):  
Marlifia Berhitu ◽  
Semuel Unwakoly ◽  
Y Manoppo
Keyword(s):  
Student Learning ◽  
Learning Outcomes ◽  
Test Data ◽  
Hypothesis Test ◽  
Student Learning Outcomes ◽  
Learning Model ◽  
Team Games ◽  
Model Based ◽  
A Value ◽  
Comparison Research

The purpose of this research is to know thestudent learningoutcomes of grade X SMA KARTIKA XIII-1 AMBON as well as knowing there is a difference whether or not the results of student learning using cooperative type Team Games Tournament(TGT) with type Make a Match as the learning model.This research is the comparison research which the sample consists of two classes, the class was given further study of chemistry at the consept of moles materials, first classexperiment (X-1) using the cooperative type Team Games Tournament(TGT) and in secondclass experiment (X-2) using the cooperative type Make a Match as the learning model. Based on the results of the study gained note that both models of learning that can enhance the learning outcomes of students, it can be seen from the success of the qualifications obtained from both the class when there has same qualification of 75% of students are on completed qualifying, 25% of students are on failed qualifying, with average from X-1 class used Type TGT is 66.125 and X-2 class used Type Make a Match is 57.3125. Hypothesis test data derived from posttest both class indicates the value significance of 0.25 (> 0.05) this a value of H0received and value of H1 rejected so it can be concluded there is no difference in student learning outcomes are either the cooperative type Team Games Tournament (TGT)and type Make a Match as the learning model.

scholarly journals User-Oriented Approach to Data Quality Evaluation

2020 ◽  
Vol 26 (1) ◽  
pp. 107-126
Author(s):  
Anastasija Nikiforova ◽  
Janis Bicevskis ◽  
Zane Bicevska ◽  
Ivo Oditis
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Open Data ◽  
Evaluation Process ◽  
Specific Model ◽  
Third Party ◽  
Quality Model ◽  
Levels Of Abstraction ◽  
Quality Requirements ◽  
Data Object

The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

HF radar data quality requirements for wave measurement

2011 ◽  
Vol 58 (4) ◽  
pp. 327-336 ◽  
Author(s):  
Lucy R. Wyatt ◽  
J. Jim Green ◽  
A. Middleditch
Keyword(s):  
Data Quality ◽  
Radar Data ◽  
Hf Radar ◽  
Quality Requirements ◽  
Wave Measurement

Conjugate Heat Transfer Analysis for Gas Turbine Cooled Stator

2012 ◽  
Author(s):  
Kuo-San Ho ◽  
Christopher Urwiller ◽  
S. Murthy Konan ◽  
Jong S. Liu ◽  
Bruno Aguilar
Keyword(s):  
Heat Transfer ◽  
Gas Turbine ◽  
Test Data ◽  
Conjugate Heat Transfer ◽  
Fluid Dynamic ◽  
Thermal Analyses ◽  
Convection Heat Transfer ◽  
Heat Transfer Analysis ◽  
Engine Test ◽  
Adiabatic Wall

This paper explores the conjugate heat transfer (CHT) numerical simulation approach to calculate the metal temperature for the gas turbine cooled stator. ANSYS CFX12.1 code was selected to be the computational fluid dynamic (CFD) tool to perform the CHT simulation. The 2-equation RNG k-ε turbulence model with scalable modified wall function was employed. A full engine test with thermocouple measurement was performed and used to validate the CHT results. Metal temperatures calculated with the CHT model were compared to engine test data. The results demonstrated good agreement between test data and airfoil metal temperatures and cooling flow temperatures using the CHT model. However, the CHT calculations in the outer end wall had a discrepancy compared to the measured temperatures, which was due to the fact that the CHT model assumed an adiabatic wall as a boundary condition. This paper presents a process to calculate convection heat transfer coefficient (HTC) for cooling passages and airfoil surfaces using CHT results. This process is possible because local wall heat flux and fluid temperatures are known. This approach assists in calibrating an in-house conduction thermal model for steady state and transient thermal analyses.

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