Browse Topic: Mathematical models
This paper deals with the uncertainty estimation of identified frequency and damping trends of whirl flutter modes, obtained by applying system identification methods on experimental data. In particular, two different identification approaches are considered, namely the free-decay analysis by using Matrix Pencil algorithm and the Data-Driven Stochastic Subspace Identification method (SSI), applied to system response to stochastic input. The two approaches lead to as many uncertainty estimation methodologies, both leveraging the bootstrapping statistical process. A full validation procedure is then set up to assess the accuracy of such methods in correctly quantifying the uncertainty of the estimated statistics. To do so, a wing-rotor state-space linear numerical model is used to simulate system response to both dwell and stochastic inputs. The state space numerical system aims to replicate the ATTILA wing-rotor wind-tunnel model, which falls in the framework of Clean Sky 2 European
This paper presents a multi-aircraft Markov decision process congestion game to resolve multi-aircraft near midair collisions (NMACs) for small unmanned aerial vehicles (sUAVs). Two key features of this framework are: 1) it leverages the concept of strategic equilibria from game theory to define optimality in multi-aircraft near midair encounters and 2) it extends the existing NMAC metrics to stochastic formulations via the occupancy measure of a Markov decision process. This game-theoretic approach decomposes the classically centralized air traffic control objective to multiple objectives that correspond to each aircraft within the NMAC, and as result, provides an aircraft-centric notion of optimality and safety that is well-suited for distributed conflict resolutions in multi-aircraft NMACs. In addition to modeling multi-aircraft as a game, stochastic metrics that extend the deterministic notions of NMACs are explored. The safety and optimality of the Nash equilibrium multi-aircraft
Enhancing rotor efficiency has been a persistent challenge in the development of micro aerial vehicles (MAV) especially for surveillance and covert operations. This study introduces a new Hybrid Flapping Wing Rotor (Hybrid FWR) configuration inspired by insect's wing flapping mechanics to address the efficiency limitation of traditional rotor designs. Unlike traditional rotary systems that rely solely on rotational motion, the Hybrid FWR combines rotational and flapping motions to significantly enhance lift generation. A comprehensive mathematical model was developed to analyze and predict the optimal aerodynamic performance, demonstrating that the Hybrid FWR configuration achieves a substantial improvement, with a power efficiency increase of up to 2.148-fold compared to conventional micro rotorcraft. Experimental validation was conducted to confirm the theoretical predictions, identifying an optimal hybrid ratio of approximately 0.7, which effectively minimizes aerodynamic resistance
NRC developed a higher-order mathematical model structure of coupled rotor-body flapping dynamics for inflight control applications. The hybrid (rigid body fuselage state and rotating hub rotor state) 8DOF model was developed utilizing explicit measurements from a novel rotor hub state measurement system enabling estimation rotor blade dynamics. The method identified second-order rotor flap dynamics, attitude-rate and rotor flap dynamics response correlation, and response lead of rotor flap dynamics over rigid body dynamics. Reducing implementation resource burdens of past approaches, this novel rotor state measurement and modelling methodology may prove useful in applied development cycles across a spectrum of needs for articulated (helicopter) and non-articulated rotor (tiltrotor, eVTOL) aeromechanics, modelling, monitoring, and operations.
The flow behavior of the two-blade MERIT rotor in hover, focusing on both pre-stall and stall regimes, is investigated through a comprehensive numerical-experimental approach. The study leverages unsteady RANS simulations to compute rotor thrust and power polars and validates them against experimental measurements. Valuable insights are provided into the capabilities of unsteady RANS methods and modern turbulence models for predicting rotor performance across these critical operating conditions. Furthermore, the numerical model incorporates blade deformations by implementing the experimentally measured flap and torsion displacements. A more realistic depiction of the rotor's aerodynamics is provided accounting for the structural deformations of the blades under aerodynamic loads. Highfidelity simulations closely predict the experiments in pre-stall conditions while discrepancies are present when the flow exhibits extended stalled regions. Blade deformations demonstrated to have only a
The parameters of a Pitt-Peters dynamic inflow model for a rotor undergoing collective inputs were extracted from experimental measurements on a hovering rotor. The four-bladed rotor of 2 m diameter featured straight, untwisted blades and a solidity of σ = 0.010. The nominal trim condition was CT /σ = 0.07 at a speed of 840 RPM. The rotor wake was measured using phase-resolved, 2D, 3-component particle image velocimetry (PIV) over a large region of interest (0.84 m x 0.77 m), and the integrated rotor aerodynamic forces were obtained from simultaneous hub loads measurements. The frequency response of rotor inflow to rotor thrust was found by measuring the system response to a stepped-sine collective input, which included frequencies of 0.2, 0.3, 0.4, 0.6, and 0.7/rev. The thrust amplitude increased with input frequency, reaching 27.4% of the steady thrust at the highest input frequency. The inflow amplitude was 4.3% of the steady inflow at 0.2/rev and decreased to 2.0% at 0.7/rev. A
This paper describes the work performed to determine a 0.999999, 6 nines, reliable fatigue critical component life using field monitored loads. The Tie Bar of the MH-47 is substantiated by Centrifugal Force (CF), which is a direct function of rotor speed, Nr, which is a monitored parameter in the Structural Usage Monitoring System (SUMS). Six nines of reliability has been the Army target for component reliability and it is generally assumed that legacy safe-life methods are near this level of reliability. With monitored loads it is possible to develop a statistical model for loads and determine an actual reliability value. This paper presents multiple methods for the Army's first attempt at establishing a retirement time using an absolute component reliability. Reliability is gained using a reduction of the Endurance Limit and mean and standard deviations of binned loads across multiple aircraft. Most notably fatigue lives can vary widely if the independent variable reliability
Rotorcrafts frequently operate in environments with severe atmospheric turbulence, for instance transferring people offshore to and from oil rigs as well as operating from and around ships. The presence of high turbulence can deteriorate performance, stability, and controllability of the rotorcraft. Additionally, such challenging conditions also generate loads that both airframe and rotor components must withstand. Following this, it is crucial to consider the impact of these operational atmospheric conditions during rotorcrafts design and development. In this context, numerical models are a fundamental tool to provide an easier and quicker way to explore the operative envelopes of the helicopter compared to performing experimental activities. This paper presents a rotor loads correlation activity between an experimental test designed and carried out by Leonardo Helicopters in which an AW189 helicopter was placed in the wake of a C-27J Spartan aircraft and a multibody structural model
Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material
Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data
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