Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation

Julio Soria; Shahram Karami; Callum Atkinson; Minghang Li

doi:10.1007/978-3-031-62094-2_5

Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation

Julio Soria, Shahram Karami, Callum Atkinson, Minghang Li

Mechanical & Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

Abstract

Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.

Original language	English
Title of host publication	The Future of Electric Aviation and Artificial Intelligence
Subtitle of host publication	Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency
Editors	T. Hikmet Karakoc, Oleksandr Zaporozhets, Alper Dalkiran, Ali Haydar Ercan
Place of Publication	Cham Switzerland
Publisher	Springer
Chapter	5
Pages	31-39
Number of pages	9
ISBN (Electronic)	9783031620942
ISBN (Print)	9783031620935
DOIs	https://doi.org/10.1007/978-3-031-62094-2_5
Publication status	Published - 2025
Event	International Symposium on Electric Aviation & Autonomous Systems 2023 - Warsaw, Poland Duration: 5 Jul 2023 → 7 Jul 2023 https://link.springer.com/book/10.1007/978-3-031-62094-2 (Proceedings)

Publication series

Name	Sustainable Aviation
Publisher	Springer
Volume	Part F418
ISSN (Print)	2730-7778
ISSN (Electronic)	2730-7786

Conference

Conference	International Symposium on Electric Aviation & Autonomous Systems 2023
Abbreviated title	ISEAS 2023
Country/Territory	Poland
City	Warsaw
Period	5/07/23 → 7/07/23
Internet address	https://link.springer.com/book/10.1007/978-3-031-62094-2 (Proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial intelligence
Bushfires
Direct numerical simulation
Remote sensing
Thermal turbulent boundary layer

Access to Document

10.1007/978-3-031-62094-2_5

Cite this

Soria, J., Karami, S., Atkinson, C., & Li, M. (2025). Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation. In T. Hikmet Karakoc, O. Zaporozhets, A. Dalkiran, & A. Haydar Ercan (Eds.), The Future of Electric Aviation and Artificial Intelligence: Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency (pp. 31-39). (Sustainable Aviation; Vol. Part F418). Springer. https://doi.org/10.1007/978-3-031-62094-2_5

Soria, Julio ; Karami, Shahram ; Atkinson, Callum et al. / Direct Numerical Simulation of a Thermal Turbulent Boundary Layer : An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation. The Future of Electric Aviation and Artificial Intelligence: Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency. editor / T. Hikmet Karakoc ; Oleksandr Zaporozhets ; Alper Dalkiran ; Ali Haydar Ercan. Cham Switzerland : Springer, 2025. pp. 31-39 (Sustainable Aviation).

@inproceedings{eda7599425fc42f083e561226bdb2016,

title = "Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation",

abstract = "Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.",

keywords = "Artificial intelligence, Bushfires, Direct numerical simulation, Remote sensing, Thermal turbulent boundary layer",

author = "Julio Soria and Shahram Karami and Callum Atkinson and Minghang Li",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; International Symposium on Electric Aviation \& Autonomous Systems 2023, ISEAS 2023 ; Conference date: 05-07-2023 Through 07-07-2023",

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doi = "10.1007/978-3-031-62094-2\_5",

language = "English",

isbn = "9783031620935",

series = "Sustainable Aviation",

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pages = "31--39",

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Soria, J , Karami, S , Atkinson, C & Li, M 2025, Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation. in T Hikmet Karakoc, O Zaporozhets, A Dalkiran & A Haydar Ercan (eds), The Future of Electric Aviation and Artificial Intelligence: Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency. Sustainable Aviation, vol. Part F418, Springer, Cham Switzerland, pp. 31-39, International Symposium on Electric Aviation & Autonomous Systems 2023, Warsaw, Poland, 5/07/23. https://doi.org/10.1007/978-3-031-62094-2_5

Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation. / Soria, Julio ; Karami, Shahram ; Atkinson, Callum et al.
The Future of Electric Aviation and Artificial Intelligence: Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency. ed. / T. Hikmet Karakoc; Oleksandr Zaporozhets; Alper Dalkiran; Ali Haydar Ercan. Cham Switzerland: Springer, 2025. p. 31-39 (Sustainable Aviation; Vol. Part F418).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Direct Numerical Simulation of a Thermal Turbulent Boundary Layer

T2 - International Symposium on Electric Aviation & Autonomous Systems 2023

AU - Soria, Julio

AU - Karami, Shahram

AU - Atkinson, Callum

AU - Li, Minghang

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.

AB - Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.

KW - Artificial intelligence

KW - Bushfires

KW - Direct numerical simulation

KW - Remote sensing

KW - Thermal turbulent boundary layer

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U2 - 10.1007/978-3-031-62094-2_5

DO - 10.1007/978-3-031-62094-2_5

M3 - Conference Paper

AN - SCOPUS:105007037851

SN - 9783031620935

T3 - Sustainable Aviation

SP - 31

EP - 39

BT - The Future of Electric Aviation and Artificial Intelligence

A2 - Hikmet Karakoc, T.

A2 - Zaporozhets, Oleksandr

A2 - Dalkiran, Alper

A2 - Haydar Ercan, Ali

PB - Springer

CY - Cham Switzerland

Y2 - 5 July 2023 through 7 July 2023

ER -

Soria J , Karami S , Atkinson C, Li M. Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation. In Hikmet Karakoc T, Zaporozhets O, Dalkiran A, Haydar Ercan A, editors, The Future of Electric Aviation and Artificial Intelligence: Proceedings of the 2023 International Symposium on Electric Aviation & Autonomous Systems and the International Symposium on Unmanned Systems, AI, Design & Efficiency. Cham Switzerland: Springer. 2025. p. 31-39. (Sustainable Aviation). doi: 10.1007/978-3-031-62094-2_5

Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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