scholarly journals ATCO radar training assessment and flight efficiency: the correlation between trainees’ scores and fuel consumption in real-time simulations

2020 ◽  
pp. 1-17
Author(s):  
T. Rogošić ◽  
B. Juričić ◽  
F. Aybek Çetek ◽  
Z. Kaplan
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Basic Training ◽  
Air Traffic Controller ◽  
Training Assessment ◽  
Assessment Scores ◽  
Human In The Loop ◽  
Assessment Standards ◽  
Two Parameters ◽  
Real Time Simulations

ABSTRACT Air traffic controller training is highly regulated but lacks prescribed common assessment criteria and methods to evaluate trainees at the level of basic training and consideration of how trainees in fluence flight efficiency. We investigated whether there is a correlation between two parameters, viz. the trainees’ assessment score and fuel consumption, obtained and calculated after real-time human-in-the-loop radar simulations within the ATCOSIMA project. Although basic training assessment standards emphasise safety indicators, it was expected that trainees with higher assessment scores would achieve better flight efficiency, i.e. less fuel consumption. However, the results showed that trainees’ assessment scores and fuel consumption did not correlate in the expected way, leading to several conclusions.

Exergy analysis of a turboprop engine at different flight altitude and speeds using novel consideration

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ali Dinc ◽  
Yousef Gharbia
Keyword(s):  
Fuel Consumption ◽  
Thermal Efficiency ◽  
Exergy Efficiency ◽  
Specific Fuel Consumption ◽  
Flight Altitude ◽  
Fuel Flow ◽  
Turboprop Engine ◽  
Shaft Power ◽  
Two Parameters ◽  
The Relationship

Abstract In this study, exergy efficiency calculations of a turboprop engine were performed together with main performance parameters such as shaft power, specific fuel consumption, fuel flow, thermal efficiency etc., for a range of flight altitude (0–14 km) and flight speeds (0–0.6 Mach). A novel exergy efficiency formula was derived in terms of specific fuel consumption and it is shown that these two parameters are inversely proportional to each other. Moreover, a novel exergy efficiency and thermal efficiency relation was also derived. The relationship showed that these two parameters are linearly proportional to each other. Exergy efficiency of the turboprop engine was found to be in the range of 23–33%. Thermal efficiency of the turboprop engine was found to be around 25–35%. Exergy efficiency is higher at higher speeds and altitude where the specific fuel consumption is lower. Conversely, exergy efficiency of the engine is lower for lower speeds and altitude where the specific fuel consumption is higher.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

A Perspective on Future Directions in Intelligent Health Control of Gasturbines and Auxiliaries

1999 ◽  
Author(s):  
K. Eftekhari Shahroudi
Keyword(s):  
Real Time ◽  
Neural Nets ◽  
Fault Models ◽  
Time Data ◽  
Bottom Line ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Health Control ◽  
Usable Information ◽  
Performance Trends

Despite their seemingly impressive claims, current products for Condition Monitoring, Diagnostic and Decision Support Systems (CMD&D) do not provide the reliable bottom line information that end users and operators need. Instead they confuse the issue with gigabytes of logged trends, complex cause-effect matrices, fault signatures etc. The term “Intelligent Health Control” here refers to the next generation of such systems which provide usable information on: • the existence and severity of faults; • how their severity will progress with utilization; • how this progress can be influenced or controlled. In this paper the fundamental shortcomings of current approaches are discussed prior to introducing the basics of Intelligent Health Control in terms of fault models and how they can be used to close the diagnostic, prognostic and intelligent control triangle. The industry will unavoidably shift towards an “information centric” view from the currently predominant “data centric” view. Gigabytes of performance trends will no longer be relevant. Instead, reliable bottom line information will be required on how to minimize or control the costs associated with machinery health degradation or faults. In order to keep the discussion real, the current state of the art of enabling technologies are discussed, including: • Open Information Buses; • Adding real time data server functionality to the control system; • Computational Steering, Human-in-the-Loop Optimization (or semi-automatic problem solving); • Fault Models; • Faster than real time simulation; • Neural Nets.

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