Quelling Cabin Noise in Turboprop Aircraft via Active Control

Cabin noise in turboprop aircraft causes passenger discomfort, airframe fatigue, and employee scheduling constraints due to OSHA standards for exposure to high levels of noise. The noise levels in the cabins of turboprop aircraft are typically 10 to 30 decibels louder than commercial jet noise levels. However. unlike jet noise the turboprop noise spectrum is dominated by a few low frequency tones. Active structural acoustic control is a method in which the control inputs (used to reduce interior noise) are applied directly to a vibrating structural acoustic system. The control concept modeled in this work is the application of in-plane force inputs to piezoceramic patches bonded to the wall of a vibrating cylinder. The goal is to determine the force inputs and locations for the piezoceramic actuators so that: (1) the interior noise is effectively damped; (2) the level of vibration of the cylinder shell is not increased; and (3) the power requirements needed to drive the actuators are not excessive. Computational experiments for data taken from a computer generated model and from a laboratory test article at NASA Langley Research Center are provided

Aircraft interior and seat design: priorities based on passengers’ opinions

Comfort is an important factor for passengers in the selection of airlines, and electric propeller aircraft will be an important element of future sustainable aviation. In this paper, we studied the order of importance of different (dis)comfort factors regarding traveling with propeller aircraft. Two experiments were conducted, one was a simulation flight on the ground with 33 participants and the other were two real flights with 97 participants. All participants were asked to rank the importance of different (dis)comfort factors in different phases of flights. Results indicated that though there are differences between the simulation and the real flights, noise, vibration and the seat are among the most important factors regarding discomfort, and space, lighting, temperature and seat are the most important factors of comfort. The results are different to those reported from previous studies on travelling by jet, where anthropometry is the most important factor. This finding suggests a difference in passenger perception between travelling by propeller propulsion and jet engines, and casts new requirements on the aircraft interior and service design for future sustainable aviation

Aircraft interior and seat design: priorities based on passengers' opinions

Comfort is an important factor for passengers in the selection of airlines, and electric propeller aircraft will be an important element of future sustainable aviation. In this paper, we studied the order of importance of different (dis)comfort factors regarding traveling with propeller aircraft. Two experiments were conducted, one was a simulation flight on the ground with 33 participants and the other were two real flights with 97 participants. All participants were asked to rank the importance of different (dis)comfort factors in different phases of flights. Results indicated that though there are differences between the simulation and the real flights, noise, vibration and the seat are among the most important factors regarding discomfort, and space, lighting, temperature and seat are the most important factors of comfort. The results are different to those reported from previous studies on travelling by jet, where anthropometry is the most important factor. This finding suggests a difference in passenger perception between travelling by propeller propulsion and jet engines, and casts new requirements on the aircraft interior and service design for future sustainable aviation

Sensor/Actuator Selection for Gust and Turbulence Control

From aircraft fuselages and space stations to vacuum cleaners and automobiles, active control of noise and/or vibration has come of age. Determining the number of active control devices (e.g. actuators) to be placed and where they are to be placed is the prototypical location problem. However, unlike typical location problems, where the customer is readily identified and is actively engaged in the assessment of the performance of the chosen locations, the customers that active control devices serve are not so easily identified and their impact on system performance issues may be unclear. For example, consider the problem of where to locate actuators to attenuate cabin noise in a propeller driven aircraft. Clearly, the ultimate customers are the passengers who will travel in these aircraft. But to decide whether one set of actuator locations is better than another it is unlikely we will ask passengers to fly in the aircraft and fill out a questionnaire about noise levels. Instead a set of sensors (pseudo-customers) are placed and the system performance of the actuators, as measured by these sensors, is recorded. Hence, we have yet another location problem. How many sensors should there be and where should they be located? In many instances collocation of sensors and actuators is the answer but in other instances it is not. A variety of approaches have been taken to address these sensor/actuator location problems. With regard to damping vibrations in truss structures (space station prototypes) it was formulated a new noxious location problem and generated high-quality solutions with a combination of LP-relaxations and heuristic search procedures. Other related efforts are summarized the actuator location problem for a single frequency interior noise control problem was examined for an idealized aircraft cabin. A tabu search procedure was shown to generate better locations for the actuators than a modal decomposition approach. The model was extended to include multi-frequency information. The sensor location problem is addressed. In the latter article a reactive tabu search scheme was shown to dominate a static tabu search approach. Our focus here is to determine locations to control and/or sense vibrations on a truss structure. However, instead of using one of the earlier optimization models referenced in the above paragraph we adopt an experimental design approach