The following presentation consists in building a model for the point performance of an Airbus A300 600R in cruise guidance mode using flight data. The aim is to optimize flight variables in order to reduce fuel consumption of an aircraft.

1. Flight Management optimization
using Advanced Analytics
Roberto Lopez
Artificial Intelligence Techniques, SL (Artelnics)
E-mail: robertolopez@artelnics.com

2. Index
• Objectives
• Data set
• Advanced analytics
• Descriptive analytics: What happened?
• Diagnostic analytics: Why did it happen?
• Predictive analytics: What will happen?
• Prescriptive analytics: How can we make it happen?
• Conclusions

3. The aim is to build a model for the point performance of an
Airbus A300 600R in cruise guidance mode using flight data.
That model will be used to optimize flight variables in order
to reduce fuel consumption.
Objectives

4. KTAS altitude weight fuel_flow
449 33746 112714 4379
442 39080 105377 3814
490 38825 125818 6877
487 32126 148168 6427
472 35934 110504 4592
447 37111 150037 5916
488 32288 188312 10058
470 30279 121693 5215
445 31485 106139 4397
458 38219 140052 5768
486 37793 109318 5054
466 32351 173437 6773
483 31740 134850 5738
410 36863 145014 5083
442 34231 116185 4328
471 35110 117316 4784
468 34305 177697 8416
493 32510 123268 5786
465 33599 160724 6255
466 34896 133385 5186
491 31513 170705 7948
481 39556 108482 4841
476 34756 113261 4811
417 37867 115256 3893
423 37906 135597 4693
417 32829 139839 4727
489 36306 120978 5783
440 33147 165894 5845
447 31398 190068 7025
411 34418 143384 4750
406 30571 181367 6082
498 30913 179993 9232
497 32745 119919 5894
426 30354 130839 4757
474 34987 159960 7214
419 38110 132055 4509
430 33323 158779 5437
476 31058 149564 5985
457 31104 163826 5995
452 30124 103805 4603
460 33263 114811 4261
435 31278 156509 5442
455 36974 100204 3948
415 39426 127815 4466
429 33468 104772 3996
408 30865 153014 5095
438 37055 157828 6851
420 38597 118050 3995
431 30027 141618 5083
428 30638 151552 5256
433 33987 182640 8207
485 31697 183818 8613
438 31880 147167 5187
477 30793 126594 5408
469 35900 171579 9397
490 33020 137570 6243
444 32661 122998 4622
459 32474 124335 4876
481 38672 102743 4547
416 36542 154452 6052
430 31975 138219 4860
439 36106 162441 6645
424 32012 168539 5714
434 36401 107079 3891
426 38705 144114 6215
406 32952 166121 5598
404 35788 131030 4295
494 30444 189008 9316
422 37208 142544 4978
463 35607 128480 4946
453 34618 184911 9656
474 37582 101215 4298
436 34161 111282 4156
480 34055 129313 5463
The data set has ben taken from Piano X.
Data set
It contains the following variables:
• KTAS (knots)
• Altitude (ft)
• Weight (kg)
• Fuel flow (kg/h)
The analysis will be performed using Neural Designer.

5. Advanced Analytics is the examination of data using sophisticated techniques to
discover deeper insights, make predictions, or generate recommendations.
Advanced analytics

6. Basic statistics are very valuable information to know our data.
Descriptive analytics:
What happened?
Variables statistics:
The weight of an A300 varies between
100.000k g and 200.000 kg.
Fuel flow histogram:
The aircraft usually flights in
regions of low consumption.

7. Diagnostic analytics aims to know the root causes of a certain process.
Linear correlations: The variable which has more influence on the
fuel flow is the weight, and that with less influence is the altitude.
Diagnostic analytics:
Why did it happen?

8. Predictive analytics uses historical data to kow what is going to happen in the future.
Predictive analytics:
What will happen?
Neural network representing
the predictive model.
Linear regression analysis for
testing the results.

9. Prescriptive analytics optimizes decision making and shows what actions to take.
Aircraft consumption as a function of true airspeed. The plot shows that
the fuel flow first increases linearly and then increases exponentially.
The optimum operational point here could be KTAS=460.
Prescriptive analytics
How can we make it happen?

10. Advanced analytics is a powerful method to optimize the performance
and reduce the environmental impact of engineering systems.
Neural Designer is a professional tool for advanced anaytics specialized in
engineering applications.
Conclusions

11. neuraldesigner.com
Artificial Intelligence Techniques, SL
Carretera de Madrid 13
37900 Santa Marta de Tormes
Salamanca (Spain)
Telephone: +34 923 133 612 Ext.13
E-mail: info@neuraldesigner.com
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