Entire Flight Research Articles

Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning

Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning

Effects of impedance-boundary-controlled casing treatment on the fan performance with eccentric inlet swirl

An eccentric inlet swirl, a source of circumferential non-uniformity in aero-engines, can compromise the stable operational range of the compression system and potentially jeopardize the safety of the entire flight vehicle. This study experimentally examined the additional effects of an eccentric inlet swirl on an axial fan in comparison with a concentric inlet condition. Then, the effectiveness of an impedance-boundary-controlled (IBC) casing treatment (CT) in extending the stable operating range of the fan under eccentric inlets is evaluated. Steady-state loading and prestall disturbance analyses were conducted using a five-hole probe and high-frequency response pressure transducers to elucidate the instability mechanisms of fans exposed to eccentric inlets. The findings indicate that the eccentric swirl generates localized over-loading regions around the circumference, where abnormal prestall disturbances amplify in amplitude across a frequency range of 0.3 to 0.5 times the blade passing frequency. These characteristics were mitigated when IBC CT was applied over the rotor tip, allowing the fan to operate under concentric inlet conditions. The IBC CT enhances the stall margin of the fan by 9.3–19.6% in response to a range of swirl inlet conditions, suggesting its potential to address the irregularity problems in fans/compressors. The mechanisms by which IBC CT extends the stall margin are discussed from the unique perspective of evaluating steady loading and system damping.

Control allocation optimization for an over-actuated tandem tiltwing eVTOL aircraft considering aerodynamic interactions

This paper describes control allocation strategies for a tandem tiltwing electric vertical take-off and landing (eVTOL) aircraft with distributed propulsion. The control mappings are constructed as local surrogate models that relate the control effectors to the aircraft's aeropropulsive forces and moments across the full flight envelope, including interactional aerodynamics effects. Control allocation optimization problems are formulated based on force and moment commands for specific flight conditions. Specifically, a nonlinear programming formulation based on nonlinear control mapping is proposed for control allocation optimization, and the problem is solved using a sparse nonlinear optimizer. The solutions obtained from different formulations such as the minimization of force and moment residuals and the minimization of control effort are investigated. Furthermore, these solutions are compared with the solutions obtained using linear control mapping-based formulations. Results presented for the cases of forward flight, low speed flight & hover, and flight in the transition corridor suggest that considering aerodynamic interactions while formulating and solving the control allocation problem is necessary for higher levels of accuracy compared to linear approaches. One-propeller-out scenarios and moment and force generation cases are also investigated. Finally, control allocation for steady and accelerated operation in the transition corridor is discussed. The contribution of the methods and results in the paper is a nonlinear control allocation methodology for the entire flight envelope of an over-actuated tandem tiltwing eVTOL aircraft.

UAV Autopilot Design for Entire Flight Profile with Cubature Kalman Filter

UAV Autopilot Design for Entire Flight Profile with Cubature Kalman Filter

Aerothermal characterization of the CALLISTO vehicle during descent

AbstractAerothermal loads are a design driving factor during launcher development as the thermal loads directly influence thermal protection system (TPS) design and successively the possible flight trajectory and mission profiles. Recent developments in reusable launch vehicles (RLV) (e.g. SpaceX, Blue Origin) have added the dimension of refurbishment to the challenges the thermal design must consider. With the current European launcher roadmap moving towards a reusable first stage aerothermal loads may significantly change. The CALLISTO vehicle is a flight demonstrator for future reusable launcher stages and their technologies developed by a tri-national consortium and planned to fly in 2025. For this vehicle the highest heat fluxes are mainly due to heating from hot exhaust gases and heated air in the proximity of the aft bay and on the exposed structures like legs and fins. In the presented study we conducted computational fluid dynamics (CFD) studies to determine the aerothermal loads on the vehicle during descent through the landing approach corridor for both phase B and phase C aeroshapes. A defining difference to previous aerothermal databases (ATD) is that the CALLISTO demonstrator is planned to execute a series of test flights with different energy levels, as well as a final demo flight. This leads to an large parameter space the final aerothermal database needs to cover. The database development is described in detail and analysed for integral and local loads, as well as interpolation uncertainties. The final phase C database allows interpolation of interface heatfluxes for the entire flight domain (Mach number, density $$\rho )$$ ρ ) at varying angle of attack (AoA). Further the sensitivity of the plume-vehicle interaction to angle of attack, chemistry, thrust vector control (TVC) and engine throttling are investigated for a critical Mach number indicating further areas of improvement for future databases.

Numerical Method for Predicting Transient Aerodynamic Heating in Hemispherical Domes

In this research, a streamlined numerical approach designed for the quick estimation of temperature profiles across the finite thickness of a hemispherical dome subjected to aerodynamic heating is introduced. Hemispherical domes, with their advantageous aerodynamic, structural, and optical properties, are frequently utilized in the front sections of objects traveling at supersonic velocities, including missiles or vehicles. The proposed method relies on one-dimensional analyses of fluid dynamics and flow characteristics to approximate the local heat flux across the exterior surface of the dome. By calculating these local heat flux values, it is also possible to predict the temperature variations within the thickness of the dome by employing the finite difference technique, to solve the heat conduction equation in spherical coordinates. This process is iterated over successive time intervals, to simulate the entire flight duration. Unlike traditional Computational Fluid Dynamics (CFD) simulations, the proposed strategy offers the benefits of significantly lower computational time and resource demands. The primary objective of this work is to provide an efficient numerical tool for evaluating aerodynamic heating impact and temperature gradients on hemispherical domes under specific conditions. The effectiveness of the proposed method will be validated by comparing the temperature profiles derived for a standard flight scenario against those obtained from 2-D axisymmetric transient CFD simulations performed using ANSYS-Fluent 2022 R2.

Design of Entire-Flight Pinpoint Return Trajectory for Lunar DRO via Deep Neural Network

Lunar DRO pinpoint return is the final stage of manned deep space exploration via a lunar DRO station. A re-entry capsule suffers from complicated dynamic and thermal effects during an entire flight. The optimization of the lunar DRO return trajectory exhibits strong non-linearity. To obtain a global optimal return trajectory, an entire-flight lunar DRO pinpoint return model including a Moon–Earth transfer stage and an Earth atmosphere re-entry stage is constructed. A re-entry point on the atmosphere boundary is introduced to connect these two stages. Then, an entire-flight global optimization framework for lunar DRO pinpoint return is developed. The design of the entire-flight return trajectory is simplified as the optimization of the re-entry point. Moreover, to further improve the design efficiency, a rapid landing point prediction method for the Earth re-entry is developed based on a deep neural network. This predicting network maps the re-entry point in the atmosphere and the landing point on Earth with respect to optimal control re-entry trajectories. Numerical simulations validate the optimization accuracy and efficiency of the proposed methods. The entire-flight return trajectory achieves a high accuracy of the landing point and low fuel consumption.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Investigation of Hybrid Laminar Flow Control Capabilities from the Flight Envelope Perspective

The present work conducts a multifidelity aerodynamic design of a medium-range commercial aircraft wing featuring hybrid laminar flow control (HLFC) technology. The design was performed to investigate the capabilities of the HLFC, considering the entire design flight envelope, low-speed performance requirements, and various potential mission scenarios. A combination of medium-fidelity aerodynamic design tools for laminar flow control using MSES-COCO-LILO, CFD RANS for high-lift cases, the quasi-three-dimensional for database generations, and SUAVE for mission analysis were used to design and assess aircraft’s performance with the HLFC wing. Studies of the design objective demonstrated a conflict between friction and compressibility drag that strongly affects the design objective function and the wing performance at various off-design conditions. A strong dependence of HLFC on flight conditions was observed to indicate technology performance limitations and a tradeoff between airplane emissions, range, and costs. Finally, a comparison of medium- and low-fidelity performance results showed deviations in the design fuel burn of 4%, a minimum possible harmonic range difference of 3%, and direct operating costs of 2%. The outcome suggests the need for better compressibility and parasite drag models for early design stages to accurately model the HLFC technology without introducing complicated methods at the first sizing steps.

Aero-engine performance evaluation model in the entire flight envelope

Abstract An aero-engine performance characteristics mathematical model in the entire flight envelope is proposed based on the stratification analysis method and multi-condition test data. The thrust, fuel flow, low/high-pressure rotor speed, exhaust temperature, and other engine output parameters can be calculated in the entire flight envelope, according to the engine operating characteristic model and input parameters of aircraft flight altitude, velocity, ambient temperature, throttle angle, etc. Based on the evaluation model proposed in this paper, the evaluation data of an aero-engine is consistent with the test data, and the error is less than 5%. The research and model can support flight performance calculation and mission planning and can be applied to flight simulator engine modules.

Characterisation of particle single-scattering albedo with a modified airborne dual-wavelength CAPS monitor

Abstract. Atmospheric aerosols impact the Earth's climate system directly by scattering and absorbing solar radiation, and it is important to characterise the aerosol optical properties in detail. This study reports the development and validation of an airborne dual-wavelength cavity-attenuated phase-shift (CAPS) single monitor, named A2S2 (Aerosol Absorption Spectral Sizer), based on the commercial CAPS single-scattering albedo monitor (CAPS-PMSSA; Aerodyne), to simultaneously measure the aerosol optical scattering and extinction at both 450 and 630 nm wavelengths. Replaced pressure and temperature sensors and an additional flow control system were incorporated into the A2S2 for its utilisation on board research aircraft measuring within the troposphere. The evaluation of A2S2 characteristics was performed in the laboratory and included the investigation of the signal-to-noise ratio, validation of performance at various pressure levels, optical closure studies and intercomparing with the currently validated techniques. The chamber experiments show that the A2S2 can perform measurements at sample pressures as low as 550 hPa and at sample temperatures as high as 315 K. Based on the Allan analysis results, we have evaluated that the minimum detection limit of the measurements shows that the measurements have a limit accuracy of ∼ 2 Mm−1 at 450 nm and ∼ 1 Mm−1 at 630 nm for 1 Hz measurements of both scattering coefficients (σsca) and extinction coefficients (σext). The optical closure study with size-selected polystyrene latex (PSL) particles shows that the truncation error of the A2S2 is negligible for particles with particle volume diameter (Dp) < 200 nm, while, for the larger sub-micrometre particles, the measurement uncertainty of A2S2 increases but remains less than 20 %. The average factors to correct the truncation error are 1.13 and 1.05 for 450 and 630 nm, respectively. A simplified truncation correction, dependent on the scattering Ångström exponent (SAE), was developed to rectify truncation errors of the future A2S2 field measurement data. The σsca and σext measured by the A2S2 show good agreement with the concurrent measured results from the nephelometer and the CAPS particle extinction monitor (CAPS-PMex). The absorption coefficient (σabs) derived through the extinction-minus-scattering (EMS) method by the A2S2 also corresponds with the results obtained from the aethalometer. The A2S2 was successfully deployed during an aircraft measurement campaign (Atmospheric ChemistRy Of the Suburban foreSt – ACROSS) conducted in the vicinity of Paris and the surrounding regions. The average SSA measured during the entire ACROSS flight campaign is 0.86 and 0.88 at 450 and 630 nm, respectively, suggesting that light-absorbing organic aerosols play a significant role. The average SAE and absorption Ångström exponent (AAE) varied due to measurements in various pollution conditions. The results presented in this study indicate that the A2S2 instrument is reliable for measuring aerosol σsca and σext at both blue and red wavelengths, and it stands as a viable substitute for future airborne evaluations of aerosol optical properties.

Extended flexible capacity planning methodology with flight network contribution to the airline fleet assignment problem

Flexible capacity planning is crucial for the airline industry due to the high fixed cost of assets, notably aircraft ownership, and the fluctuating demand, particularly in response to external shocks like pandemics and political crises. Following the negative consequences of such events on the financial positions of almost all airline companies, there is growing interest in developing more responsive capacity planning structures for cost efficiency in the airline industry. In this study, we present a sequential two-phase methodology for flexible capacity planning and airline fleet assignment considering the factors related to fleeting strategies, passenger demand behavior, and contribution of flights in the network. The first phase of the methodology generates potential scenarios to estimate the number of flights to the destinations, based on the data and the reports provided by two prominent associations in the aviation industry, which incorporate various country-related and airline-related factors, including population, income, routes, airports, and connectivity. The second phase identifies the required number of aircraft for each type within the fleet inventory, taking into account the aircraft utilization rates associated with each scenario. The fleet assignment problem is also reformulated to employ the objective of maximizing total profit of the entire flight network by introducing network contribution and nation/region-specific limitations in this phase. A real case study from Turkey is conducted to illustrate efficiency of the proposed methodology. We also present a comparative study to highlight the impact of network contribution and how fleet size affects the outcomes. We have observed that the inclusion of the network contribution aspect and country constraints has led to a significant improvement in financial indicators and connectivity. The results show that the methodology can effectively tackle challenges in airline transportation problems.

Real-time optimization control of variable rotor speed based on Helicopter/ turboshaft engine on-board composite system

In order to adequately leverage the advantages of coaxial compound helicopters (CCH) to enhance overall performance and efficiency through variable rotor speed, and to improve the fuel economy of the composite propulsion system, a real-time optimization control method of variable rotor speed based on helicopter/turboshaft engine on-board composite system is proposed. Firstly, a steady-state model of the CCH required power and an on-board inverse model of the turboshaft engine based on an improved deep neural network propulsion system model (AdamDNN-PSM) are established. On this basis, an on-board adaptive model based on adaptive square root Kalman filter (ASRUKF) estimator is employed to correct the precision of the on-board inverse model and to track the real engine state. Finally, by adopting the feasible sequence quadratic programming (FSQP) method, an optimal speed rotor (OSR) is obtained through real-time optimization control, which reduces fuel consumption during cruising while considering both the geometric and aerodynamic constraints of the rotor. The simulation results demonstrate that the accuracy and real-time performance of the on-board inverse model have been significantly improved. The real-time optimization control method for OSR effectively reduces fuel consumption during cruising and enhances the CCH performance of the entire flight envelope.

Aircraft Wing Morphing Using Auxetic Structures

Cellular materials exhibit two key properties: structures and mechanisms. This allows for the design of structures using cellular materials while effectively controlling both stiffness and flexibility, based on the connectivity of the struts. This study aims to explore the in-plane flexible properties of cellular materials dominated by bending under macroscopic deformation. Additionally, it seeks to establish a method for designing a passive morphing airfoil with flexible cellular cores. The investigation focuses on airfoils featuring re-entrant and S-shaped cellular cores, analyzing their behavior under static loads by examining the deformation of the cellular cores subjected to aerostatic loads. In the context of the airfoil's deformation with flexible cellular cores under aerostatic loading, shear emerges as the predominant deformation mode for the cores of the airfoil. Wings of conventional aircraft are optimized for only a few conditions, not for the entire flight envelope. Therefore, it is necessary to develop the morphing airfoil with smart structures for the next generation of excellent aircraft. In this project, this was made possible using, re-entrant and S-shaped auxetic structures as a member of the meta- material family, with negative Poisson's ratio to enable an effortless passive morphing mechanism as it has high flexibility along in-plane direction (chord-wise). The 3D CAD Models of Re-entrant and S-shaped auxetic airframes were designed and analyzed. Initially, Static Structural analysis is performed on both airframes to observe the structure’s behavior, and design modification and optimization are performed in different iterations. With a reduction in maximum equivalent stress by 20%, the Re-entrant airframe exhibits lower stress and hence more flexibility. The wings were modeled with a span of 1m using auxetic airframes, air pressure was generated using CFD analysis with MACH 0.45. Finally, the fluid-structure interaction was done by importing the air pressure and performing static structural analysis for the structural performance of wings using auxetic airframes. It was found that Re-entrant auxetic wing showed an increase of 9.99% in load carrying capacity, accompanied by a decrease of 389 grams of weight when compared to S-shaped auxetic wing. Considering the deformation of the airframe with flexible cellular cores under a load, the re-entrant honeycomb core shows the highest flexibility in shear and causes lower stress than the S-auxetic cores. This implies that the re-entrant honeycomb core has the potential for passive morphing.

Improving the calculation module for estimating pollutant emission from conventional and hybrid regional aircraft

For the aviation sector, it is extremely important to devise revolutionary solutions in the field of technology to restrain the potential impact of civil aviation on the environment to the level of the established strategic goals of ACARE (FlightPath2050). The introduction of innovative technologies (improvement of the combustion chamber, the introduction of electric hybrid power plants on airplanes, and the use of alternative aviation fuel) will ensure the sustainable growth of air transportation. To assess the effectiveness of advanced technologies, it is extremely important to have a model for calculating global/local emissions that takes into account the parameters of the flight path of conventional and hybrid aircraft, operational characteristics of the aircraft engine and hybrid powerplant, and the features of sustainable fuel. The improved module for calculating emission indices by combining the module for calculating the parameters of the flight path and the results of calculating the thermogas-dynamic calculation of the aircraft engine makes it possible to detect the influence of fuel consumption (engine thrust) on the values of the emission indices. This feature is representative for evaluating the efficiency of hybrid powerplants because the electrification of the aircraft fleet is primarily aimed at reducing fuel consumption. The analysis of simulation results reveals that the fuel consumption and EINOx are significantly reduced (for the climb stage – 25 %; for the descent stage – 30 %) for the hybrid AN26 compared to the conventional AN26. The specified operational measure, in the part of the low-pitch descend, significantly reduces EICO for the hybrid AN26 by an average of 50 % compared to the descend stage for the conventional trajectory. The results of calculations for the entire flight path demonstrate that the use of a hybrid power plant for An26 contributes to an average reduction of fuel consumption by 10 %, NOx emissions by 25 %, water vapor emissions by 10 %, and CO2 by 10 %.

A deep learning-based approach for predicting in-flight estimated time of arrival

Predictability is key for efficient and safe air traffic management. In particular, accurately estimating time of arrival for current passenger flights may help terminal controllers to plan ahead and optimize airport operations in terms of safety and resource allocation. While traditional physics-based simulations are still widely used, they are complex to model and often fail to include many factors affecting the progress of a flight. In this paper, we propose a deep learning approach based on LSTM that leverages the 4D trajectory of the flight and weather data at the destination airport, to accurately predict estimated time of arrival. We evaluate our model on flights arriving at Adolfo Suárez-Madrid Barajas airport (Spain), in the first three quarters of 2022, achieving a mean absolute error of 2.65 min over the entire flight and reporting competitive short- and long-term predictions at different spatial and temporal horizons.

Full-Envelope Flight Control for Compound Vertical Takeoff and Landing Aircraft

This paper presents a flight control design for compound vertical takeoff and landing (VTOL) vehicles. With their multitude of degrees of controllability as well as the significant variations in their flight characteristics, VTOL vehicles present challenges when it comes to designing their flight control system, especially for the transition phase where the vehicle transitions between near-hovering and high-speed wing-borne flights. This work extends previous research on the design of unified and generic control laws that can be applied to a broad class of vehicles such as hovering vehicles and fixed-wing aircraft. This paper exploits this unifying property and presents an extension for the case of compound VTOL vehicles. The proposed control approach consists of nonlinear geometric control laws that are continuously applicable over the entire flight envelope, excluding the use of switching policies between different control algorithms. A transition strategy consisting of a sequence of high-level set points is associated with the flight control laws; it is defined with respect to flight envelope limitations and is applied in this work to a commercially available compound unmanned aerial vehicle. The control algorithms are implemented on a Pixhawk controller; they are evaluated via hardware-in-the-loop simulations and finally validated in a flight experiment.

Experimental and numerical gust identification using deep learning models

Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SynopsisThe utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.

Environmental Chamber Characterization of an Ice Detection Sensor for Aviation Using Graphene and PEDOT:PSS.

In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors' heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.