Browse Topic: Neural networks
The advent of electric propulsion technology has led to a paradigm shift in aircraft design over the past few decades. This shift has expanded the possibilities for design and optimization processes more than at any previous time. To support these advancements, efficient flight dynamics simulation models that can be employed in iterative optimization and design processes are essential. Among the modules of a typical flight dynamics framework—namely, control, flight dynamics, and aerodynamics—the aerodynamics module, which includes the rotor performance model, generally demands the most computational effort, thereby limiting simulation efficiency. In this study, a novel machine learning (ML)-assisted flight dynamics framework is developed, incorporating a Neural Network Blade Element Theory (NN-BET) model as the rotor performance module. The results show a 7- to 8-fold reduction in computational time compared to fast, physics-based frameworks utilizing efficient Blade Element Momentum
This paper discusses the development of a quantitatively-accurate non-linear hybrid flight dynamics model of a hover-capable Air-Launched Tailsitter Unmanned Aerial System (ALUAS) in order to 1) understand its dynamics during complicated maneuvers, and 2) provide a high-fidelity framework to develop novel control laws. Wind tunnel tests were conducted on a 1:1 scale model of the full aircraft to measure the airloads, which were used in the simulation as a lookup table. Flight tests of the ALUAS were performed in hover, transition, and cruise to collect a large amount of unique state measurements by providing large excitations to induce highly transient motion. The flight dynamics predictions using Rotorcraft Comprehensive Analysis System (RCAS) software were then compared with experimental flight test data. To correct any discrepancies in the RCAS physics-based predictions, a correction was learned from the experimental measurements, making use of the large amount of collected flight
This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response features and increasing the number of hidden neurons, multiple-input single-output (MISO) architectures significantly improved accuracy and reduced Root Mean Square Error (RMSE). Further enhancement was achieved by implementing bidirectional LSTM (BiLSTM) layers, which reduced both delay and transient error. Comparisons with inverted linear time-invariant (LTI) approximations showed that neural
Blade–wake interaction (BWI) is a significant source of broadband noise and is often dominant in rotors with high blade counts. Accurately capturing the resulting unsteady blade loading is computationally expensive and, therefore, drives the cost of BWI noise calculation. To address this challenge, a low-fidelity BWI noise prediction tool was developed using aerodynamic data from the blade element momentum theory (BEMT) and the lattice Boltzmann method (LBM) for a series of rotor configurations with medium to high solidity. Starting from a six-bladed baseline rotor, 13 additional configurations were generated by varying blade twist, taper, root collective, solidity, and blade count. The relationship between vortex miss distance and blade loading unsteadiness was quantified to construct a semi-empirical BWI noise model. The model predicted BWI noise with a root mean square error of 3.9 dBA and a mean absolute percentage error of 1%. It was subsequently integrated into a BEMT framework
This paper discusses the development of a flight dynamics model (or digital twin) of a compact and re-configurable coaxial-propeller-based micro air vehicle (MAV) in hover, edgewise, and maneuvering flight using a hybrid physics-based plus data-driven approach. The MAV has a mass of 366 grams (0.81 lb), and features a 52 mm (2.05 in) diameter cylindrical fuselage, foldable propellers, and a two-axis gimbal thrust vectoring mechanism for pitch and roll control. The aircraft has been successfully launched from a pneumatic cannon and has achieved stable and controlled flight. A physics-based flight dynamics model of this novel MAV has been developed using Rotorcraft Comprehensive Analysis System (RCAS). RCAS is able to predict the translational dynamics near hover reasonably well; however, the accuracy decreases for rotational dynamics in edgewise flight resulting in significant differences between predicted dynamics and flight test data, known as residual dynamics. The current hybrid
In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM
This study investigates the use of machine learning (ML) models to estimate the gross weight (GW), the longitudinal position of the center of gravity (CGx), and 1/rev cyclic flapping angles (Δ1c and Δ1s) of a compound helicopter with three redundant controls - main rotor RPM, collective propeller thrust, and stabilator angle. Neural Network (NN), Gaussian Process for Regression (GPR), and Support Vector Machine (SVM) algorithms are employed to develop estimation models using supervised training. The airspeed, redundant controls, main rotor controls, aircraft attitudes, and main rotor torque are selected as input variables (predictors) to the models due to their accessibility through the aircraft Health and Usage Monitoring System (HUMS). The dataset is split into low-speed and high-speed regimes to compare the prediction accuracy and training cost of separate regime models against a combined full-regime model. Separate airspeed regime GPR models showed superior performance in GW
Rotor blade optimization presents a multifaceted challenge as traditional design methodologies rely on computationally exhaustive high-fidelity computational fluid dynamics (CFD). Conversely, low-fidelity techniques such as potential flow based codes are inaccurate, especially in the regions of flow separation. This paper proposes leveraging artificial neural networks (ANNs) to predict the performance polar of a given airfoil geometry, and to facilitate the inverse design of airfoil, a modified form of ANNs (known as Tandem Neural Networks (T-NNs)) is implemented. The airfoil inverse design is a multi-point optimization problem (at multiple angles of attack) and therefore, the T-NNs are trained on the vectors of performance polar instead of individual angles of attack. The paper also delves into a comprehensive analysis of data wrangling, airfoil parametrization and design of experiments to cover a wide range of rotorcraft airfoils. A novel way of including practical design constraints
This paper investigates the feasibility of using machine learning to predict whirl flutter bifurcation diagrams. The machine learning techniques selected for the study are XGBoost and the long short-term memory neural network. These techniques are selected for their suitability for sequential and nonlinear data. The techniques are investigated for a propeller-nacelle test case with polynomial structural nonlinearities resulting in supercritical or subcritical whirl limit-cycle oscillations. The techniques are trained to learn the bifurcation diagram for the amplitude variation of pitch angle limit-cycle oscillations of the propeller-nacelle system as a function of the forward speed for various levels of cubic structural nonlinearity. Bifurcation diagram learning and testing data are generated using the bifurcation forecasting method. XGBoost is computationally faster to train but less accurate for low amounts of learning data, especially for the most weakly and strongly nonlinear cases
As part of maintenance improvement on helicopters, Airbus Helicopters has made available a proactive analysis service based on Health and Usage Monitoring System data generated during the flight. The present paper describes the new approach used to detect and classify any changes in time series behavior thanks to A.I. (Artificial Intelligence) especially computer vision. This new approach is more efficient and relevant than the classical approach based one statistical law [Ref 1]; in fact, it is acting, as the human eye, which is able to identify easily any abrupt change on the time series, and classifies it, whether Machine learning or Deep Neural Networks both have shown excellent results in term of classification accuracy. First part of this paper highlights how the learning data were prepared, then the second and the third parts give more details about how the time series are transformed into image presentation and how the different Artificial Intelligence models were selected and
Tailsitter configurations that operate in both fixed and rotary wing flight modes are typically capable of generating large control forces and moments, making them inherently capable of rapid transitions and aggressive maneuvers. However, harnessing these capabilities requires feedback control strategies that can effectively estimate the non-linear aerodynamics loads involved to successfully exploit them. This paper describes initial steps in combining an onboard flow sensing strategy with a data-driven approach to estimating inflight air loads. A neural network is trained to use measurements from a multi-hole probe to predict the output from a set of pressure sensors embedded in a wing section undergoing a series of pitch motions in a wind tunnel. We hypothesize that this limited context of emulating a sensor network represents a focused and compartmentalized approach to applying emerging data-driven techniques to challenging aeronautical problems. We compare estimation results from a
The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading
In the field of aerodynamics, there is a growing need for rapid load prediction in engineering applications. Surrogate modeling offers a promising solution, providing faster results compared to high-fidelity computational models. This study focuses on a Machine Learning (ML) framework tailored for surrogate modeling, specifically for integrated aerodynamic load predictions in aircraft design. Central to this framework is a Deep Neural Network (DNN) component capable of handling both steady-state and fluctuating aerodynamics. A key challenge for surrogate models lies in maintaining prediction accuracy, especially in scenarios involving nonlinear flow phenomena like flow separation and transonic shifts. To address these challenges, we introduce a two-step physics-state predictor that integrates an intermediate Convolutional Neural Network (CNN) component. This approach enhances the surrogate model's capability to accurately represent dynamic separated flows and other nonlinear patterns
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