Perspectives and Trends in Flight Dynamics and Simulation

1. Introduction

In today’s aeronautical sciences, flight mechanics, flight dynamics, and simulation form a fundamental disciplinary and methodological core that enables us to interpret, anticipate, and drive innovation in the field. These fields are not only related to the creation of analytical tools that describe how an aircraft behaves; they provide the conceptual and theoretical foundation that allows new design ideas to evolve into reliable and mature aeronautical solutions. In a sector where technological progress is continuous and expectations are high, research in these domains acts as a true engine of advancement.

Aviation is currently experiencing a particularly dynamic phase, in which both incremental progress and disruptive concepts coexist. Environmental [1,2], operational [3], and economic [4,5] pressures are pushing the industry toward increasingly ambitious targets [6,7,8,9,10], and effective responses require a clear understanding of aeromechanical phenomena and the ability to model, simulate, and integrate them within highly complex design frameworks. Air transport must reconcile safety, performance, cost efficiency, and reduced environmental impact; research supports this evolution by expanding the set of models, methods, and analysis tools used to evaluate emerging technological pathways with technical depth and numerical reliability.

Environmental sustainability remains one of the most influential drivers of innovation [11,12]. Reducing both climate impact [13,14] and local emissions [15] motivates the development of new propulsion concepts and redesigned aircraft architectures. Significant efforts are being devoted to hydrogen-powered systems [16,17,18], fully electric propulsion for short-range operations [19,20], hybrid electric solutions [21,22,23], and a variety of advanced aero-propulsive integrations [24,25,26]. Concepts based on boundary layer ingestion [27,28], propulsive fuselages [29], distributed propulsion [30,31], or direct aerodynamic–propulsive coupling aim to unlock new performance margins. Alongside these, unconventional aircraft architectures, such as box-wing configurations [32], blended wing bodies [33], or truss-braced wings [34], offer alternative pathways to improve aerodynamic efficiency. Research on advanced materials, manufacturing techniques [35], and multifunctional or lightweight structures [36] further extends the range of possible innovations, making it feasible to reduce mass, enhance efficiency, and introduce new degrees of design freedom.

Safety, traditionally the central pillar of air transport, continues to stimulate technological progress [37,38,39]. More robust and adaptive flight control systems [40], the improved management of complex aero-propulsive architectures, and the increasing use of high-fidelity simulation to analyze nonlinear or off-design behaviours all contribute to safer flight operations. Developments in next-generation fly-by-wire systems, fault-tolerant control algorithms, predictive maintenance supported by digital twin approaches [41,42,43], and integrated load management strategies are reshaping how aircraft handle uncertain or degraded conditions. Understanding dynamic characteristics, nonlinear interactions, and extended flight envelopes is essential for validating the safety of new configurations.

Cost reduction, both during development and operational use, represents another important factor influencing modern innovation. Airlines and manufacturers demand aircraft that are not only energy-efficient but also easier to maintain, operate, and integrate into existing fleets. This has accelerated the adoption of advanced simulation tools that replace portions of physical testing, the use of design-to-cost strategies embedded in MDO frameworks [44,45,46], and the development of digital platforms that optimize mission profiles [47,48,49], control strategies, and energy management [50,51]. Flight dynamics plays a key role also in this context, as the ability to predict aircraft behaviour allows engineers to refine operations, reduce consumption, and improve the economic viability of new concepts.

All of these goals rely on the inherent interdisciplinary complexity of aircraft design. New aeronautical concepts cannot be evaluated in isolation: aerodynamics, structures, propulsion, flight control, mission requirements, and cost considerations interact from the earliest stages of design. Modern MDO techniques have reached a level of maturity that allows integrated analysis across large design spaces, enabling systematic comparisons between alternative technological solutions [52]. Nonetheless, the core of any multidisciplinary assessment remains the aeromechanical characterization of the aircraft, that is, the ability to characterize its performance and to describe its dynamic response accurately and coherently across a wide set of operating conditions.

In this regard, flight mechanics serves as the fundamental language through which aircraft performance is interpreted. Building reliable dynamic models, identifying key aerodynamic parameters [53,54], understanding stability and controllability characteristics [55,56], and analyzing dynamic regimes are essential steps in turning innovative concepts into viable industrial candidates [57,58]. Simulation complements this work by providing a platform to test, refine, and validate behaviours in both expected and challenging scenarios [59,60,61,62,63]. Today, simulation spans an extensive spectrum: from low-fidelity tools suitable for fast conceptual iterations, to high-fidelity models coupling CFD, aeroelasticity, and nonlinear dynamics; from pilot-in-the-loop simulators to virtual environments for testing control laws; and from nominal mission evaluations to degraded/uncertain conditions.

Modern simulation environments make it possible to replicate realistic flight scenarios, assess advanced control strategies, explore complex aerodynamic interactions, and examine coupled aero-propulsive–structural effects with increasing accuracy. Methods such as virtual flight testing, reduced-order modelling for CFD–flight dynamics coupling, uncertainty quantification techniques, and the real-time integration of control models are now essential tools for quantifying the potential of novel aeronautical technologies.

Flight mechanics, flight dynamics, and simulation form an integrated methodological ecosystem that allows researchers and engineers to evaluate, with rigour and depth, the true potential of emerging innovations in aviation. Without their methodological and disciplinary role, new ideas would remain conceptual sketches; with them, they can be measured, compared, optimized, and translated into feasible industrial solutions. It is within this perspective that the Second Edition of the Special Issue “Flight Dynamics, Control & Simulation”, available here [64], was conceived as a scientific platform dedicated to advancing methods, models, and research contributions that will help guide the next generation of aircraft and operational concepts. In the following section, an overview of the studies published in this Special Issue is provided.

2. Overview of the Published Articles

A total of 11 contributions were included in this Special Issue, addressing flight dynamics and simulation from a broad and diversified perspective, with case studies and methods drawn from multiple research areas. The paragraph below provides an overview of these works, outlining their scope and emphasizing the key results achieved.

Ref. [65] introduces an adaptive control approach for multirotor UAVs based on a backstepping sliding-mode control scheme enhanced with a Radial Basis Function Neural Network to estimate and compensate for external disturbances and model uncertainties in real time. The method ensures a robust tracking performance under variable flight conditions, including wind gusts and payload changes. The proposed controller is validated through hardware-in-the-loop simulations and real flight tests, demonstrating improved stability, trajectory tracking, and disturbance rejection compared with traditional PID and backstepping controllers. The main innovation lies in combining adaptive neural estimation with non-singular sliding-mode dynamics, achieving smooth control action without chattering while maintaining robustness. This contributes to enhancing autonomous flight reliability and precision in UAV operations, particularly in complex or uncertain environments, and provides a practical reference framework for real-world multirotor applications.

Ref. [66] investigates fuel sloshing dynamics in aircraft wing tanks through a hybrid methodology combining 1D simulations, CFD modelling, and artificial neural networks (ANNs). The study analyses how geometric and operational parameters, such as baffle configuration, cutout diameter, hole number and position, barrier usage, and fuel volume fraction, affect the centre of gravity deviation and retreat time during maneuvers. A dataset of 252 1D simulations is processed using a deep neural network to identify the dominant parameters influencing sloshing behaviour, which were later verified via 3D multiphase CFD simulations. The results reveal that barrier usage has the strongest effect on both CG deviation and recovery time, followed by fuel volume fraction and cutout geometry. The proposed ANN-assisted framework efficiently highlights the critical design variables governing fuel motion, enabling the faster optimization of baffle layouts to enhance flight stability and minimize centre of gravity fluctuations in aircraft fuel systems.

Ref. [67] introduces a novel grey box identification method for modelling microturbojet dynamics, integrating data-driven clustering and hybrid time–frequency regression. This approach effectively separates stochastic and causal components from experimental data and constructs low-order Wiener-structured models that capture both static and dynamic nonlinearities. The innovation lies in combining data mining-based cluster analysis with physically interpretable modelling, yielding models that outperform their Hammerstein–Wiener and neural network counterparts in accuracy, robustness, and error propagation resistance. Experimental validation demonstrates excellent agreement with measured turbojet behaviour across all operating regimes. The research provides computationally efficient, control-compatible models suitable for UAV propulsion applications, improving predictive power and extending control range while maintaining compliance with certification-oriented modelling standards.

Ref. [68] provides a two-stage optimal input design method for aircraft system identification using multi-stage Pseudo-Random Binary Sequence (PRBS) inputs and Maximum Likelihood Estimation applied to an aircraft flight dynamic model. In stage I, the initial PRBS parameters are optimized using spectral features and aerodynamic constraints; in stage II, the Fisher Information Matrix, D-optimality, and Crest Factor are integrated to refine PRBS frequency, amplitude, order, and periodicity. This innovation mitigates the non-convexity and model over-parameterization typical of heuristic inputs. The results show over 95% accuracy using a Single Sequence Band-Limited PRBS, outperforming traditional input types. The method enables the accurate extraction of longitudinal aerodynamic parameters, enhancing control law design, dynamic analysis, flight safety, and simulator fidelity for supersonic aircraft.

Ref. [69] discusses a quaternion-based robust sliding-mode controller (RSMC) for quadrotors operating under severe wind disturbances. The main innovation lies in using quaternion representation to avoid Euler angle singularities and ensure precise attitude control, combined with a robust sliding-mode approach that compensates for aerodynamic disturbances modelled through realistic deterministic–stochastic wind profiles. Stability and convergence are mathematically proven using the Lyapunov theory. Simulations in two challenging wind scenarios (steady turbulent winds and abrupt directional shifts) show that the proposed controller achieves fast convergence (<10 s), superior trajectory tracking, and reduced oscillations compared with a conventional PD controller. Moreover, it maintains rotor speeds within feasible limits, enhancing energy efficiency and operational reliability for quadrotor applications in harsh environments.

Ref. [70] numerically investigates the self-sustained roll oscillations (“wing rock”) of an 80° delta wing using unsteady RANS simulations with a Dynamic Fluid–Body Interaction (DFBI) and overset mesh framework in STAR-CCM+. The innovative contribution lies in demonstrating that modern CFD methods can accurately predict both regular and chaotic self-oscillatory roll modes over a wide range of angles of attack, including those involving vortex breakdown. The simulations reproduce the experimental results both qualitatively and quantitatively, revealing two distinct oscillation attractors caused by asymmetric vortex dynamics. The key impact was as follows: the work validates CFD as a predictive tool for nonlinear aerodynamic instabilities, clarifies the bifurcation mechanisms driving wing rock onset, and provides an efficient CFD-based methodology for extracting aerodynamic stability derivatives to anticipate self-oscillatory behaviour.

Ref. [71] investigates the motion and control of a quadcopter carrying a spherical payload containing an internal cavity partially filled with liquid. The study introduces a modified pendulum model to simulate the fluid dynamics, accounting for the quasi-steady and oscillatory components of the liquid mass. Aerodynamic drag, including wind effects, is incorporated, a feature often neglected in prior studies. The research develops normalized dynamic equations and proves the controllability and observability of steady flight through linearization and matrix analysis. An optimal feedback control strategy based on the Riccati equation ensures trajectory tracking and the suppression of oscillations in both the payload and internal liquid. The results provide practical guidance for stabilizing copter load systems with internal fluid dynamics, extending its applicability to aerospace and transport operations.

Ref. [72] introduces a novel framework combining Neighbourhood Component Analysis for feature extraction and Genetic Programming-based Symbolic Regression for physically interpretable aerodynamic modelling. The study addresses the limitations of black box data-driven methods and low-data scenarios typical of flight testing. Validated on simulation (NASA Twin Otter) and real flight data (UAV and X-rudder layout vehicle), the approach successfully identifies compact analytical models where the pitching moment deviation depends solely on the angle of attack. Compared with support vector machine learning and Gaussian process models, it achieves up to 37% and 80% accuracy improvements for the UAV and X-rudder layout vehicle, respectively. The innovation lies in combining dimensionality reduction with interpretable symbolic modelling, yielding accurate, generalizable physical laws from limited, high-dimensional aerodynamic data.

Ref. [73] provides an improved cubic polynomial fitting algorithm to address calibration inaccuracies in the large-stroke blade pitch control of a tiltrotor aircraft. Traditional linear methods fail to capture the strong nonlinear relationship between actuator stroke and control angles when pitch variations exceed 40°, leading to significant errors. The proposed multivariate cubic regression model effectively models these nonlinearities, achieving a 57% reduction in collective pitch error and a 33% reduction in cyclic pitch error compared with linear fitting. Experimental validation confirms that the accuracy reaches the sensor’s physical limit (0.02°). The research’s main innovation lies in combining geometric modelling and efficient polynomial fitting to enable high-precision, low-cost, and field-applicable calibration, enhancing tiltrotor control accuracy, stability, and flight safety.

Ref. [74] reviews and refines classical stability and control standards to create screening metrics for the preliminary design of high-speed and hypersonic aircraft. Its innovation lies in unifying legacy experimental, computational, and flight test data from vehicles such as the X-15, SR-71, XB-70, HL-10, and Space Shuttle into a consistent analytical framework applicable to modern “bank-to-turn” configurations. The study introduces quantitative discriminators for lateral directional stability, coupling, and control response bandwidth, emphasizing the need for strong bare airframe directional stability and the early detection of control coupling and inertia coupling risks. Its impact is providing validated, low-risk design metrics that bridge classical flight quality criteria and contemporary hypersonic flight control development.

Ref. [75] proposes an innovative bank-to-turn control system for hypersonic re-entry vehicles that integrates a single moving mass with differential ailerons, eliminating ablation-prone elevators and rudders while improving internal space use. The research develops a coupled 7-DOF model that quantifies inertial coupling between mass motion and rolling dynamics, defining stability boundaries for safe maneuvering. A dynamic inversion controller with L1 adaptive augmentation is designed to reject aerodynamic uncertainties, actuator degradation, and high-frequency disturbances. Monte Carlo simulations show a 20.6% reduction in roll tracking error and 72% suppression of oscillations compared with the baseline control. This composite actuation concept offers a robust, thermally resilient solution for future HRV designs, enhancing maneuverability and control reliability under extreme re-entry conditions.

3. Concluding Remarks

Research in flight mechanics, flight dynamics, and flight simulation continues to play a decisive role in advancing aeronautical sciences. These disciplines provide the analytical and methodological foundations needed to understand how innovative concepts behave, how they interact with the surrounding environment, and how they can be reliably integrated into increasingly complex design and operational frameworks. Their contribution is essential not only for interpreting the performance of new aircraft architectures, but also for guiding technological development in a way that remains consistent with safety, efficiency, and sustainability requirements.

The wide variety of contributions included in this Special Issue reflects the vitality and breadth of this research domain. Although the papers explore diverse topics, from advanced control strategies for UAVs to aerodynamic modelling, nonlinear dynamics, system identification, and high-fidelity simulations, they share a common scientific thread. Each of the studies, in their own way, enhances our ability to model, predict, or control the dynamic behaviour of aerial vehicles. Together, they demonstrate how progress in flight dynamics-related disciplines can directly support innovation across the entire aeronautical landscape, strengthening the methodological tools that enable researchers and engineers to address new challenges with confidence and precision.

Looking ahead, the central role of these topics is expected to become even more prominent. The ongoing transformation of aviation will require the continuous refinement of the models, methods, and simulation frameworks used to study aircraft behaviour. Flight mechanics and simulation will remain at the heart of this transformation, offering the rigorous and flexible tools needed to validate emerging ideas, compare alternative technological pathways, and ensure that innovation proceeds hand in hand with reliability and safety. In light of this strong scientific interest, a Third Edition of the Special Issue “Flight Dynamics, Control & Simulation” has now been opened and is available here [76]. Researchers working on these topics are warmly invited to contribute their most recent advancements. The aim is to continue fostering a scientific space where methodological developments, innovative applications, and interdisciplinary perspectives can converge, supporting the evolution of aeronautical research and inspiring the next generation of technological breakthroughs.