Samwise Aeronautical Mechanics
Saturday, 11 July 2026
Journal Watch
This week’s top peer-reviewed research from the AIAA Journal, Aerospace Science and Technology, Aeronautical Journal, and allied publications. In-depth summaries of the papers that matter for aeronautical mechanics.
Atmospheric Turbulence Dynamics and Control of Tailless UCAVs Modelled Under Von Karman Disturbances
Tailless UCAVs with lambda wing planforms lack vertical tail surfaces, which reduces inherent directional stability and creates strong coupling between longitudinal and lateral-directional flight modes. Without conventional stabilising surfaces, disturbance rejection becomes a core design challenge rather than a secondary consideration. This paper examines how continuous atmospheric turbulence affects both sets of dynamics for such a vehicle and evaluates whether advanced control strategies can maintain acceptable flying qualities under realistic disturbance conditions. The study linearised the UCAV dynamics using small perturbation theory, representing the system in state-space form. Atmospheric turbulence was modelled via the Von Karman power spectral density formulation at three intensities — light, moderate, and severe — benchmarked against MIL-F-8785C flying quality criteria. Three controllers were designed and compared: a state-feedback pole-placement controller, a state observer-based design, and a model predictive controller (MPC). MPC proved the most capable architecture, rejecting turbulence effectively at all three intensities while maintaining accurate pitch and roll tracking. The simpler pole-placement controllers showed measurable degradation in attitude tracking at higher turbulence levels, confirming the limitations of purely linear designs for this vehicle class. The findings validate that tailless UCAV configurations can meet mission-oriented flight quality standards when the control architecture explicitly accounts for the strong mode coupling inherent in vertical-tail-less designs. For control law developers, the results establish MPC as a leading candidate for disturbance-robust autopilot design in lambda-wing UCAVs and provide a validated Von Karman modelling framework directly applicable to similar tailless configurations. Published in The Aeronautical Journal (Cambridge), 11 June 2026.
Sources: The Aeronautical Journal Share ↗ ✉︎ Email 💬 Text
Generalised Contrail Formation Theory Identifies Climate Risks for Fuel-Cell-Propelled Aircraft
Contrail formation from conventional kerosene combustion is well characterised, but the growing shift toward hydrogen and ammonia fuels — and fuel-cell propulsion systems — introduces substantially higher exhaust moisture content that alters contrail formation propensity in ways not fully captured by classical thermodynamic theory. Understanding these differences has become urgent as hydrogen aircraft development accelerates. This paper presents a generalised thermodynamic framework describing the mixing of moist aircraft exhaust with ambient air, enabling determination of whether contrails form under given atmospheric conditions. The classical Schmidt-Appleman mixing line formulation is compared against a novel generalised approach, and both are used to derive the limiting ambient temperature above which contrail formation is suppressed across fuel types and propulsion architectures. The inaccuracies inherent in the classical mixing line largely cancel each other, producing negligible differences between the two approaches for standard contrail prediction. However, when heat and water vapour recuperation systems — proposed to improve fuel-cell efficiency — reduce exhaust thermal energy, contrail formation propensity increases markedly. For high-hydrogen-content fuels, plumes with reduced heat content can reach supersaturation values exceeding 500%, triggering direct gas-phase condensation to liquid droplets: a process absent in conventional contrail formation. This could substantially increase ice crystal numbers and amplify contrail-cirrus climate forcing. The analysis quantifies specific exhaust water vapour reduction requirements that suppress contrail formation in fuel-cell aircraft, providing actionable design parameters for propulsion engineers developing hydrogen aviation powerplants. Published in The Aeronautical Journal (Cambridge), 17 June 2026. DOI: 10.1017/S0001924026101857.
Sources: The Aeronautical Journal Share ↗ ✉︎ Email 💬 Text
Adaptive Two-Stage FEM Updating Framework Cuts Aircraft Modal Frequency Error to 2.39%
Finite element model updating (FEMU) for complex flexible aircraft structures typically demands large numbers of direct finite element evaluations to achieve reliable parameter convergence — a computational burden that renders the technique impractical for routine aeroelastic design and structural validation workflows where engineering time and compute cost are constrained. This paper proposes an adaptive two-stage surrogate framework combining Bayesian regularisation artificial neural networks (BR-ANN) with a trust-region resampling strategy. In the global stage, a coarse surrogate is constructed via Latin hypercube sampling (LHS), and Bayesian inference guides the non-dominated sorting genetic algorithm II (NSGA-II) in identifying the Pareto-front region containing promising candidate solutions. A trust-region strategy then performs high-fidelity resampling within the local neighbourhood of the knee-point solution, establishing a refined surrogate that directly addresses the smoothing bias inherent in global surrogates and improves local parameter convergence. Validation on the GARTEUR SM-AG19 benchmark aircraft demonstrates the approach’s effectiveness: average modal frequency error decreased from 4.56% to 2.39% while physically interpretable updating of material and equivalent beam parameters was maintained throughout. The method mitigates the smoothing effect imposed by conventional global surrogates without sacrificing computational efficiency. For structural analysts, the framework offers a practical route to high-fidelity structural model correlation for complex flexible airframes, delivering a validated baseline suitable for subsequent aeroelastic predictions. The Bayesian regularisation approach preserves parameter interpretability, an important property when updating physically meaningful structural properties rather than abstract fitting coefficients. Published in The Aeronautical Journal (Cambridge), 29 June 2026.
Sources: The Aeronautical Journal Share ↗ ✉︎ Email 💬 Text
Curated by JD · samwise.agency
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