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Aerospace
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Flight Dynamics, Control & Simulation (3rd Edition)
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Flight Dynamics, Control & Simulation (3rd Edition)

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 411

Special Issue Editor

Dr. Karim Abu Salem

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile, Ambientale e Meccanica, DICAM, Università degli Studi di Trento, Via Mesiano, 77, 38123 Trento, TN, Italy
Interests: aircraft design; green aviation; aerodynamics; flight mechanics; innovation; multidisciplinary optimization; flight dynamics; new aircraft concepts; hybrid-electric aircraft
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research in the field of transport aviation constantly faces new, complex, and ambitious challenges. Increasingly, new aircraft concepts, novel types of propulsion, innovative techniques for aircraft control, and overall disruptive innovations are being studied, investigated, and developed. The study of flight dynamics has always been of particular relevance when investigating the behavior of innovative transport aircraft, assessing their stability and controllability characteristics, and evaluating their performance. Depending on the level of fidelity used, flight simulation models, methods, and tools enable the characterization of the aeromechanical behavior of aircraft at any stage of the design process, from initial conceptual stages to the most advanced detailed analysis. Such models are relevant to the advancements in different fields of transport aeronautics, such as enhancing flight safety, optimizing mission performance, developing new concepts for aircraft operations (e.g., urban air mobility), and establishing virtual certification methods. This Special Issue aims to collect as broadly as possible the most up-to-date contributions regarding the application of flight dynamics models for the characterization of transport aircraft aeromechanic features. In particular, great emphasis is placed on the development and application of simulation models to analyze the performance of aircraft with a high degree of innovation, whether in terms of architecture, systems, or propulsion. In addition, the development and validation of new methodologies for aeromechanical analysis and optimization, advanced simulation, novel flight control techniques, and flight dynamics analysis tools for multidisciplinary design workflows represent significant contributions to increasing knowledge in the field.

Dr. Karim Abu Salem
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • flight dynamics
  • performance analysis
  • flight simulation
  • advanced controls
  • new aircraft concepts
  • innovation
  • multidisciplinary optimization
  • flight mechanics

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Published Papers (1 paper)

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Research

22 pages, 2182 KB  
Article
Physics-Informed Graph Neural Network for Flight Dynamics Modeling
by Liang Ma, Zhanwu Li, Juntao Zhang, You Li and Shijie Deng
Aerospace 2026, 13(5), 471; https://doi.org/10.3390/aerospace13050471 - 16 May 2026
Viewed by 73
Abstract
Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation capability, and difficulty in assimilating flight test data. This paper proposes an [...] Read more.
Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation capability, and difficulty in assimilating flight test data. This paper proposes an architectural integration of physics-informed neural networks (PINNs), graph neural networks (GNNs), and known flight mechanics equations for flight dynamics modeling. Without requiring aerodynamic coefficient labels, the method predicts flight state derivatives using state-transition data. The approach encodes the structural knowledge of flight mechanics equations into graph topology and a physics computation layer (PhysicsLayer), so that the neural network only needs to learn the unknown aerodynamic coefficients while all remaining physical relationships are computed by the governing equations. Using an F-16 fighter six-degree-of-freedom model as the verification platform, an ablation study involving Direct-MLP, PINN, PIGNN, and GNN is conducted. Results show that the PIGNN architecture improves single-step derivative prediction accuracy by 86.6% over Direct-MLP, 60.9% over pure PINN, and 90.8% over GNN. In 499-step (approximately 5 s) rollout state prediction, the PIGNN Core RMSE is 1.1554, with approximately linear error growth within the first 100 steps indicating well-controlled short-range error accumulation. The graph-structural prior enables the network to learn aerodynamic coefficients that closely match the F-16 reference aerodynamic database without aerodynamic coefficient supervision. The results demonstrate that combining graph-based dependency modeling with hard physical constraints is effective for interpretable flight dynamics surrogate modeling. Full article
(This article belongs to the Special Issue Flight Dynamics, Control & Simulation (3rd Edition))
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