Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Uzun, Mevlüt; Demirezen, Mustafa Umut; Tsourdos, Antonios

doi:10.3390/aerospace8020044

Cited by 19 publications

(6 citation statements)

References 39 publications

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“…Uzun el al. [18] tried to combine physics-guided deep-learning models with fuel consumption modeling in an attempt to improve data-driven models' consistency exactly in conditions where it is not covered by data, showing, first the importance of having data, and second, that combining domain knowledge with advanced computational techniques can significantly enhance the accuracy and reliability of fuel measurement systems in aviation, further supporting the objectives of this project.…”

mentioning

confidence: 79%

Computational Tool for Aircraft Fuel System Analysis

Di Marzo,

Calil,

Najafabadi

et al. 2024

Aerospace

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Fuel level gauging in aircraft presents a significant flight mechanics challenge due to the influence of aircraft movements on measurements. Moreover, it constitutes a multidimensional problem where various sensors distributed within the tank must converge to yield a precise and single measurement, independent of the aircraft’s attitude. Furthermore, fuel distribution across multiple tanks of irregular geometries complicates the readings even further. These issues critically impact safety and economy, as gauging errors may compromise flight security and lead to carrying excess weight. In response to these challenges, this research introduces a multi-stage project in aircraft fuel gauging systems, as a continuum of studies, where this first article presents a computational tool designed to simulate aircraft fuel sensor data readings as a function of fuel level, fuel tank geometry, sensor location, and aircraft attitude. Developed in an open-source environment, the tool aims to support the statistical inference required for accurate modeling in which synthetic data generation becomes a crucial component. A discretization procedure accurately maps fuel tank geometries and their mass properties. The tool, then, intersects these geometries with fuel-level planes and calculates each new volume. It integrates descriptive geometry to intersect these fuel planes with representative capacitive level-sensing probes and computes the sensor readings for the simulated flight conditions. The method is validated against geometries with analytical solutions. This process yields detailed fuel measurement responses for each sensor inside the tank, and for different analyzed fuel levels, providing insights into the sensors’ signals’ non-linear behavior at each analyzed aircraft attitude. The non-linear behavior is also influenced by the sensor saturation readings at 0 when above the fuel level and at 1 when submerged. The synthetic fuel sensor readings lay the baseline for a better understanding on how to compute the true fuel level from multiple sensor readings, and ultimately optimizing the amount of used sensors and their placement. The tool’s design offers significant improvements in aircraft fuel gauging accuracy, directly impacting aerostructures and instrumentation, and it is a key aspect of flight safety, fuel management, and navigation in aerospace technology.

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confidence: 79%

Computational Tool for Aircraft Fuel System Analysis

Di Marzo,

Calil,

Najafabadi

et al. 2024

Aerospace

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“…Many machine learning algorithms are able to automatically extract complicated relationships from data. 26 However, in some fields, machine learning models were inadequate to explain the reality of the studied systems while others were not able to capture the complexity of the studied physical phenomena. 16 This is mainly due to the nature of the problems themselves as well as the type of data gathered from the field; .e.g.…”

Section: Theory Guided Machine Learningmentioning

confidence: 99%

Forecasting cross-tie condition based on the dynamic adjacent support using a theory-guided neural network model

Soufiane,

Zarembski,

Palese

2023

Proceedings of the Institution of Mechanical Engineers, Part F:

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Cross-ties represent a key infrastructure asset of the railroad industry. Recent research has shown that the cross-tie life is not only affected by the traditionally defined load and track design parameters but also by support condition, and in particular, support condition as represented by the condition of adjacent cross-ties. This paper builds upon the recent research and is focused on predicting a cross-tie’s future condition as a function of the changing condition of the surrounding cross-ties. As accurate cross-tie condition information becomes available from automated inspection systems, this data allows for the development of a theoretical framework for predicting cross-tie degradation and corresponding cross-tie life. This theoretical framework allows for the modeling of the interactions between adjacent cross-ties as a complex and dynamic system. Thus, the objective of this paper is to develop a model that uses theory guided machine learning framework as supported by well-defined railroad engineering relationships, such as the Beam on Elastic Foundation theory, to forecast the cross-tie condition as a function of its adjacent cross-ties and their corresponding degradation rates. The resulting model outperformed a more conventional traditional neural network model. The theory guided machine learning model showed very good correlation with actual data exhibiting an R2 of 88.6% and an a20-index of 91% suggesting that the incorporation of domain knowledge into the machine learning model leads to demonstrably better cross-tie life prediction results.

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“…The authors in [350] use physics-guided recurrent graph networks to model the flow and the temperature in rivers and enforce the model to respect local patterns via physics-guided regularization. In [351], an energy conservation constraint is integrated while [734] apply physical regularization in fuel consumption modeling.…”

Section: Knowledge Integration Via Auxiliary Lossesmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Cited by 19 publications

References 39 publications

Computational Tool for Aircraft Fuel System Analysis

Computational Tool for Aircraft Fuel System Analysis

Forecasting cross-tie condition based on the dynamic adjacent support using a theory-guided neural network model

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

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