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Biodynamic feedthrough (BDFT) occurs when vehicle vibrations and accelerations feed through the pilot's body and cause involuntary motion of limbs, resulting in involuntary control inputs. BDFT can severely reduce ride comfort, control accuracy and, above all, safety during the operation of rotorcraft. Furthermore, BDFT can cause and sustain Rotorcraft-Pilot Couplings (RPCs). Despite many studies conducted in past decades -both within and outside of the rotorcraft community -BDFT is still a poorly understood phenomenon. The complexities involved in BDFT have kept researchers and manufacturers in the rotorcraft domain from developing robust ways of dealing with its effects. A practical BDFT pilot model, describing the amount of involuntary control inputs as a function of accelerations, could pave the way to account for adversive BDFT effects. In the current paper, such a model is proposed. Its structure is based on the model proposed by Mayo [1], its accuracy and usability are improved by incorporating insights from recently obtained experimental data. An evaluation of the model performance shows that the model describes the measured data well and that it provides a considerable improvement to the original Mayo model. Furthermore, the results indicate that the neuromuscular dynamics have an important influence on the BDFT model parameters.

The aim of this study is to assess the load predicting capability of a classical BeddoesLeishman dynamic stall model in a horizontal axis wind turbine (HAWT) environment, in the presence of yaw-misalignment. The dynamic stall model was tailored to the HAWT environment, and validated against unsteady thick airfoil data. Subsequently, the dynamic stall model was implemented in a blade element-momentum (BEM) code for yawed flow, and the results were compared with aerodynamic measurements obtained in the MEXICO (Model Rotor Experiments under Controlled Conditions ) project on a wind turbine rotor placed in a large scale wind tunnel. In general, reasonable to good agreement was found between the BEM model and MEXICO data. When large yawmisalignments were imposed, poor agreement was found in the downstroke of the movement between the model and the experiment. Still, over a revolution the maximum normal force coefficient predicted was always within 8% of experimental data at the inboard stations, which is encouraging especially when blade fatigue calculations are being considered.

At the heart of a flight simulator resides the mathematical representation of aircraft behaviour in response to control inputs, atmospheric disturbances and system inputs including failures and malfunctions. While this mathematical model can never be wholly accurate, its fidelity, in comparison with real world behaviour, underpins the usefulness of the flight simulator. The present paper examines the state of the art achieved in validating mathematical models for helicopter simulators, addressing the strengths and weaknesses of the present European standard for the qualification of helicopter flight simulators, JAR FSTD-H (previously JAR-STD-1H/2H/3H). Essential questions are examined, such as: What is the required model fidelity to guarantee a simulation is sufficiently representative to be fit for purpose? Are the tolerances set in the current standards fine enough that they lead to only minor changes in handling qualities? What is an acceptable tuning process for the simulation? What is the effect of modelling fidelity on the overall pilot control strategy? What is the relationship between the settings of the simulator cueing

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Abstract:The aviation community relies heavily on flight simulators as a fundamental tool for research, pilot training and development of any new aircraft design. The goal of the present 1 Corresponding author Tel. +31 15-2785374, E-mail Address m.d.pavel@tudelft.nl 2 paper is to provide a review on how effective ground simulation is as an assessment tool for unmasking adverse Aircraft-and-Rotorcraft Pilot Couplings (APC/RPC). Although it is generally believed that simulators are not reliable in revealing the existence of A/RPC tendencies, the paper demonstrates that a proper selection of high-gain tasks combined with appropriate motion and visual cueing can reveal negative features of a particular aircraft that may lead to A/RPC. The paper discusses new methods for real-time A/RPC detection that can be used as a tool for unmasking adverse A/RPC. Although flight simulators will not achieve the level of reality of in-flight testing, exposing A/RPC tendencies in the simulator may be the only convenient safe place to evaluate the wide range of conditions that could produce hazardous A/RPC events.

Helicopter performance relies heavily on the available output power of the engine(s) installed. A simplistic single-variable analysis approach is often used within the flight-testing community to reduce raw flight-test data in order to predict the available output power under different atmospheric conditions. This simplistic analysis approach often results in unrealistic predictions. This paper proposes a novel method for analyzing flight-test data of a helicopter gas turbine engine. The so-called "Multivariable Polynomial Optimization under Constraints" (MPOC) method is capable of providing an improved estimation of engine performance and maximum available power. The MPOC method relies on optimization of a multivariable polynomial model subjected to equalities and inequalities constraints. The Karush-Khun-Tucker (KKT) optimization method is used with the engine operation limitations serving as inequalities constraints. The proposed MPOC method is applied to a set of flight-test data of a Rolls Royce/Allison MTU250-C20 gas turbine engine, installed on a MBB BO-105M helicopter. It is shown that the MPOC method can predict the engine output power under a wider range of atmospheric conditions and that the standard deviation of the output power estimation error is reduced from 13hp in the current single-variable method to only 4.3hp using the MPOC method (over 300%