Browse Topic: Simulation and modeling
The Sikorsky S-92® helicopter fleet, representing more than 300 aircraft and 2.6 million flight hours, is relied upon to support a large range of important missions across the globe. In previous efforts, a high-fidelity CFD-CSD based full-aircraft simulation methodology, co-simulated with production FCS, was developed and applied to model both coaxial aircraft and single main/tail rotor configurations (Refs. 1-5). The CFD solver is based on the CREATE™-AV HELIOS toolset (Ref. 6) and the CSD solver is based on Rotorcraft Comprehensive Analysis System (RCAS) (Ref. 7). The current paper further correlated the CoSim methodology (Ref. 1) with the S-92® helicopter flight-test database at both hover, cruise and edge-of-envelope maneuver flight conditions. The consistent correlations for flight dynamics, static and fatigue component loads at conditions across the flight envelope demonstrate the reliable predictive capability of the high-fidelity CoSim methodology to be-used as a virtual
RPM-controlled hexacopters offer mechanical simplicity and inherent redundancy, but are unable to re-trim under all failure cases in forward flight. This paper investigates the use of reverse-enabled rotors as a means of expanding the attainable trim envelope and improving fault tolerance in RPM-controlled hexacopters. Isolated rotor experiments are conducted to characterize thrust and torque behavior under forward and reverse rotation, providing validation data for aerodynamic modeling. A blade-element-based model implemented in the Rensselaer Multicopter Analysis Code (RMAC) is then used to perform comprehensive trim analyses for a 1200-lb-class hexacopter in hover and in cruise at the best-range speed of 65 kts. Post-failure trim solutions are evaluated for four configurations, including edge-first and vertex-first orientations with different rotor spin directions. Results show that enabling reverse rotation allows trim recovery for all single-rotor failure cases in cruise
Dimensional reduction of data can be accomplished through various methods and has applications critical to machine learning and surrogate modeling. Within the rotorcraft community, leveraging these techniques allows for improved rotor parameterization and performance prediction. Machine learning models generally perform faster and better with lower input dimensions, so long as all necessary information is retained, making appropriate dimension reduction paramount. Data can also be arranged in a one-dimensional (concatenated/stacked) or two-dimensional arrays to take advantage of function correlations, and this arrangement may allow for greater reduction at lower reconstruction costs. Principal Component Analysis with a stacked input shape proves to be the most effective reduction method considered, with reconstruction accuracy being validated though a suite of mid-fidelity aerodynamic simulations. A blade geometry defined using 204 original parameters can be fully described using just
The induced and profile power of a hovering rotor was evaluated using experimental and computational methods. Momentum theory principles were coupled with experimental measurements over a range of thrust conditions to characterize the induced and profile power consumption at low Reynolds number conditions ∼ 105. An empirical induced power factor, κi, was extracted to quantify the non-ideal losses. Results show that these losses increase as the Reynolds number reduces, and nearly twice the power is required at Retip = 0.27×105 than the ideal momentum theory prediction. These results were compared with high-fidelity computational fluid dynamics simulations using the partial-pressure field (PPF) force/power decomposition to extract the induced and profile power contributions of the rotor. The PPF method decomposes the static pressure field of a numerical Reynolds-averaged Navier-Stokes solution into Euler and dissipative partial pressure fields. Simulations were performed across a range
A high-fidelity computational study is conducted to investigate the aerodynamic behavior and flight response of an electric Vertical Take-Off and Landing (eVTOL) multirotor configuration using unsteady computational fluid dynamics (CFD) framework. Four simulation cases are considered to examine the vehicle aerodynamics under both prescribed and fully coupled conditions. Prescribed hover and forward-flight cases isolate rotor aerodynamics and rotor-airframe interactions under constrained kinematics. Six-degree-of-freedom (6-DoF) free-flight maneuvering simulations capture the coupled evolution of aerodynamic loads, vehicle attitude, and translational motion. The results demonstrate that the high-fidelity unsteady CFD framework, coupled with rigid-body dynamics, effectively resolves the tightly coupled aerodynamic–dynamic interactions inherent to eVTOL configurations. This work provides a foundation for future investigations into trim strategies, control modeling, and expanded flight
This paper presents an initial handling qualities analysis of an Electric Vertical Take-Off and Landing (eVTOL) hexacopter. The analysis uses the Distributed Electric Propulsion Simulation (DEPSim), developed by Penn State University (PSU) and the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM), developed by Continuum Dynamics, Inc. (CDI). The study focuses on evaluating a generic AAM hexacopter performing Handling Qualities Task Elements (HQTE) as defined by the DOT / FAA. A trajectory controller was developed to enable simulation of prescribed flight paths, allowing automated simulation of four HQTEs: Heliport Approach, Hovering Turn and Hold, Pirouette, Lateral Reposition and Hold. Design modifications incorporating lateral mast tilt and Direct Side Force Control (DSFC) were implemented to enhance yaw control and ride qualities. Piloted simulations were conducted at the PSU rotorcraft flight simulation facility using DEPSim, employing an Attitude Command Attitude
This paper discusses the design of a 2000-lb manned eVTOL aircraft propelled by a novel cycloidal rotor propulsion system. To systematically evaluate the performance of the proposed configuration, a coupled trim model was developed to quantitatively evaluate the performance of the configuration across a range of forward flight speeds. The trim framework integrates an efficient physics-guided neural-network-based aerodynamic model for cycloidal rotor performance with a vehicle-level dynamic response model. This framework is used to conduct a systematic parametric study to identify key cycloidal rotor and airframe design parameters. The selected configuration is verified using high-fidelity CFD simulations, and a detailed structural design, powertrain design, and CAD model of the aircraft is developed. In addition to CFD validation, the proposed cycloidal rotor underwent structural optimization to confirm the validity of such a concept at this scale. The results demonstrate that the
This paper presents a reinforcement learning (RL)–based outer-loop controller for quadrotor UAV trajectory tracking and its real-world experimental validation. The proposed approach integrates RL into a standard cascaded flight-control architecture by replacing the conventional PID outer loop while retaining the onboard attitude and body-rate PID controllers. This hierarchical design preserves reliable inner-loop stabilization while leveraging RL to address nonlinear dynamics, coupling effects, and modeling uncertainty in translational motion. The controller is trained entirely in a physics-based simulation using Proximal Policy Optimization (PPO) and transferred directly to a Crazyflie quadrotor without additional tuning. Performance is evaluated through real-world figure-8 trajectory tracking experiments with varying time scales to impose increasing dynamic demands. Compared to a conventional PID outer-loop controller operating under identical conditions, the RL-based controller
The present study explores the active vibration suppression of a lift-offset (L.O.) coaxial rotor system in high-speed forward flight by applying a multicyclic controller with individual blade control (IBC) actuation. A high-fidelity vibration analysis is conducted through a loose-coupling (LC) framework that combines a compressible 3D (three-dimensional) CFD (Computational Fluid Dynamics) solver with a comprehensive aeromechanics (CA) method. Since the upper and lower rotor experience different aerodynamic environments, an asynchronous or different IBC actuation for each rotor is applied to achieve greater vibration reduction performance than the usual synchronous or identical actuation. Open-loop control results indicate that the asynchronous actuation suppresses the vibratory loads more effectively, from which the best actuation inputs are identified and subsequently incorporated into the more involved closed-loop control. The validity of closed-loop controller is verified across
Helicopter blades are often modeled as one-dimensional (1D) beams and considered to undergo medium-to-large deformations. The degree of nonlinearity that a beam theory can handle greatly affects prediction accuracy. In this work, quantitative evaluations are made for static and dynamic behavior of beams and blades using the classic moderately-large deformation beam (MLB) model as reference to a geometrically exact beam (GEB) model. A rotorcraft aeromechanics analysis framework is constructed to incorporate both beam models. The framework contains various solution procedures such as trim, blade response, loads, and vibrations while allowing external interface to high-fidelity computational fluid dynamics (CFD) analysis. A validation study is performed to examine the extent of the accuracy in the large deformation behavior of benchmark beam problems in static and dynamic conditions. Next, the HART (Higher-harmonic Aeroacoustic Rotor Test) II rotor is applied to evaluate the relative
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